ORIGEN: Zero-Shot 3D Orientation Grounding in Text-to-Image Generation
Authors: Yunhong Min
Daehyeon Choi
Kyeongmin Yeo
Jihyun Lee
Minhyuk Sung
Venue: NeurIPS 2025 poster ()
Keywords: Controlled image generation
Primary area: applications

Abstract: We introduce ORIGEN, the first zero-shot method for 3D orientation grounding in text-to-image generation across multiple objects and diverse categories. While previous work on spatial grounding in image generation has mainly focused on 2D positioning, it lacks control over 3D orientation.
To address this, we propose a reward-guided sampling approach using a pretrained discriminative model for 3D orientation estimation and a one-step text-to-image generative flow model. While gradient-ascent-based optimization is a natural choice for reward-based guidance, it struggles to maintain image realism. Instead, we adopt a sampling-based approach using Langevin dynamics, which extends gradient ascent by simply injecting random noise—requiring just a single additional line of code.
Additionally, we introduce adaptive time rescaling based on the reward function to accelerate convergence. Our experiments show that \textsc{Origen} outperforms both training-based and test-time guidance methods across quantitative metrics and user studies.

ORIGEN: Zero-Shot 3D Orientation Grounding in Text-to-Image Generation
Yunhong Min
Daehyeon Choi"
Kyeongmin Yeo KAIST
Jihyun Lee
Minhyuk Sung
{dbsghd363, dachyeonchoi, aaaaa, jyun lee, www.aannaaiistt.ae.... @kaisk.
"a toddy bear holding stick nest to toy
bas driving down reaily
"a dog is sitting deross in backreat
mun,
motorcycle,
cat, giruffi,
Figure 3D orientation-grounded text-to-image generation results of ORIGEN. We present ORIGEN, the first zero-shot method for 3D orientation grounding in text-to-image generation across multiple objects and diverse categories. ORIGEN generates high-quality images that are accurately aligned with the grounding orientation conditions (colored arrows) and the input text prompts.
Abstract
We introduce ORIGEN, the first zero-shot method for 3D orientation grounding in text-to-image generation across multiple objects and diverse categories. While previous work spatial grounding in image generation has mainly focused on 2D positioning, it lacks control over 3D orientation. To address this, we propose reward- reward-guided sampling approach using a pretrained discriminative model for 3D orientation estimation and a one-step text-to-image generative flow model. optimization natural choice for reward-based guid- ance, it struggles to maintain image realism. Instead, we adopt a sampling-base based approach using Langevin dynamics, which extends gradient ascent by simply in- jecting random noise cnise-requiring just single additional line of code. Additionally, we introduce adaptive time rescaling based on the reward function to accelerate convergence. Our experiments show that ORIGEN outperforms both training-based and test-time guidance methods across quantitative metrics user studies. Project Page: https://orgeen2225giibuu
While gradient-ascent
Introduction
Controllability is a key aspect of generative models, enabling precise, user-driven outputs. In image generation, spatial grounding ensures structured and semantically meaningful results by incorporating conditions that cannot be fully specified through text alone. Recent research integrating
spatial instructions, such bounding boxes [1-5] and segmentation masks 3 g 9 has
"quual contribution.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

shown promising results. While these works have advanced 2D spatial control, particularly positional constraints, 3D spatial grounding remains largely unexplored. In particular, orientation essential for defining an object's s spatial pose 10-16] yet its integration into conditioning remains an open challenge.
A few existing methods, such as Zero-I-to-3 17 and Continuous 3D Words [8] support orientation- conditioned image generation. However, Zero-I-to-3 enables only relative orientation control with respect to a reference foreground image, while Continuous 3D Words is limited to single-object images and supports only half-front azimuth control. Moreover, all these models lack realism because they are trained on synthetic data, i.e., multi-view renderings of centered 3D objects, as real-world training images with accurate per-object orientation annotations are not publicly available. In addition, OrientDream [19] supports orientation control via text prompts, but it is restricted to four primitive azimuths (front, left, back, right) and is also limited to single-object images.
To overcome these limitations, we propose ORIGEN, the first method for generalizable 3D orientation grounding in real-world images across multiple objects and diverse categories. We introduce zero-shot approach that leverages test-time guidance from OrientAnything [20]. a foundational discriminative model for 3D orientation estimation. Specifically, using pretrained one-step text-to- image generative model (21] that maps a latent vector to a real image, along with reward function defined by the discriminative model, our goal is find latent vector whose corresponding real image yields high reward.
A natural approach for this search is gradient-ascent-Bee based optimization [2 but it struggles keep the latent distribution aligned with the prior (a standard Gaussian), leading to a loss of realism in the generated images. To address this, we introduce sampling- approach that balances reward maximization with adherence to the prior latent distribution. Specifically, we propose a novel method that simulates Langevin dynamics, where the drift term determined by our orientation-groundinng reward. We further show that its Euler-Maruyama discretization simplifies to a surprisingly simple formulation-an extension of standard gradient ascent with random noise injection, which can be implemented in a single line of code. To further enhance efficiency, we introduce a novel time- rescaling method that adjusts timesteps based on the current reward value, accelerating convergence.
Since no existing method has quantitatively evaluated 3D orientation grounding in text-to-image generation (except for user studies by (18), we curate a benchmark based the MS-COCO dataset (23] mixing and matching object classes and orientations to create images with single or multiple orientation- grounded objects. We demonstrate that ORIGEN significantly outperforms previous orientation-condiiioone image generative models [8] 17 on both our benchmark and user studies. Since prior models cannot condition on multiple objects (whereas ORIGEN can, as shown in Fig. D. comparisons are conducted under single-object conditioning. Additionally, we perform experiments to further validate the superior performance of our method over text-to-image generative models with orientation-specifie prompts and other training-free guided sampling strategies.
Overall, our main contributions are:
We present ORIGEN, the first method for 3D orientation grounding in text-to-image generation for multiple objects across diverse categories.
We introduce novel noward-guider sampling approach based on Langevin dynamics that provides a theoretical guarantee for convergence while simply adding single line of code.
We also propose reward-adaptiv time rescaling method that accelerates convergence.
We show that ORIGEN achieves significantly better 3D orientation grounding than existing orientation--condtionee image generative models [88 17), text-to-image generative models 24
25 21 with orientation-specific prompts, and training-free guided sampling strategies.
2 Related Work
Viewpoint or Orientation Control. Several works have focused on controlling the global viewpoint of the entire image. For example, Burgess et al. (26) propose wew-maaping network that predicts a word embedding to control the viewpoint in text-to-image generation. Kumari el al. (27 enable model customization to modify object properties via text prompts, with added viewpoint control. However, these methods cannot individually control the orientation of foreground objects. Other works have attempted to control single-object orientation in image generation. For instance, Liu et

---

al. [17] introduce an image diffusion model that controls the relative orientation of an object with respect its reference image. Huang et al. [9] propose an -eentation-oondtiioned image diffusion model for sampling muet object images for text-to-3D generation. The most recent work in this domain, Cheng et al. [88, aim to control object attributes, including azimuth, through continuous word embeddings. However, these methods rely on training-base bhaiing---eedd--e approaches using single-object synthetic training images, limiting their generalizability across multiple objects and diverse categories. We additionally note that few works address image generation conditioned on depth (28 or 3D bounding boxes BT. but they do not allow direct control of object orientations - for example, 3D bounding boxes have front-back ambiguities.
Training-Fres Guided Generation. A number of truining-free methods have been proposed for guided generation tasks. DPS [32.. MPGD (3 and Pi-GDM [34] update the noisy data point at each step using a given reward function. FrecDoM (35 takes this further by introducing rewinding, where intermediate data points are regenerated by reversing the generative denoising process. The core principle of this approach is leveraging the posterior mean from noisy image at intermediate step via Tweedie's 's formula [36]. The expected future reward can also be computed from the posterior mean, allowing gradient ascent update the noisy image. However, key limitation of these methods is that the posterior mean from diffusion model is often too blurry to accurately predict future rewards. While distilled or fine-tuned diffusion (3 and flow models [41 42) can mitigate this issue, their straightened trajectories lead to insufficiently small updates to the image during gradient ascent intermediate timesteps.
Recent approaches [22 (45 attempt to address this limitation by updating the initial noise rather than the intermediate noise. Notably, ReNO 22 is an optimization method based on one-step generative model to efficiently iterate the initial noise update through one-step generation and future reward computation. However, gradient ascent with respect to the initial noise often suffers from local optima and leads to deviations from real images, even with heuristic regularization [46]. To overcome this limitation, we propose novel sampling-based approach rather than an optimization-basee one, leveraging Langevin dynamics to effectively balance reward maximization and realism.
Boaining-Based Guided Generation. For controlling image generation, ControlNet IP-Adapter [47] are commonly used to utilize pretrained generative model to control for various conditions, though they require training data. For 3D orientation grounding, no public real-world training data is available, and collecting such data would be particularly challenging, especially for multi-object grounding, due to the need for diversity. To address this, recent works have introduced RL-based reward fine-tuning approaches 488 -511 However, these methods require substantial computational resources and, more importantly, cause significant deviation from the pretrained data distribution when trained with task-specific rewards, leading to degraded image quality and reduced diversity [52]. Hence, instead of fine-tuning, we propose a test-time reward- -grddedd framework that leverages a discriminative foundational model for guidance.
28 and
3 ORIGEN
We present ORIGEN, a zero-shot method for 3D orientation grounding in text-to-image generation. To the best of our knowledge, this is the first 3D orientation grounding method for multiple objects across opea-vocybulary categorics.
3.1 Problem Definition and Overview
Our goal is to achieve 3D orientation grounding in text-to-image generation using a one-step genera- tive flow model 53 Fo. based on test-time guidance without fine-tuning the model. Let I Fx c) denote an image generated by Fo given an input text and a latent x sampled from prior distribution q = NN(0, I). The input text e prompts the generation of an image containing a set of desired objects (e.g.. "A person in brown suit is directing a dog"). For each of the N objects that appear in we associate a set of object phrases W = { [w,]] ) (e.g., ("dog", "person") and a corresponding set of 3D orientation grounding conditions & = {Ø. N Following the convention [20], each object orientation o; is represented by three Euler angles Φ; = {Φi. 13 1== azimuth Φ;,1 E [0. 360), polar angle Φr.2 E [0, 180), and rotation angle 61.3 E [0, 360). The goal of 3D orientation ground- ing to generate an image in which the N objects appear with their corresponding orientations N.N
+ = 1
3

---

Why Test-Time Guidance? Supervised methods are not applicable to this task due to the lack of training datasets containing diverse real-world images with per-object orientation annotations. Previous supervised approaches [8 17 have therefore been limited to single-object scenarios within a narrow set of categories. To address this, we propose training-free approach that leverages recent foundational models to design reward function: GroundingDIIN (54) for object detection and OrientAnything [20] for orientation estimation. While this reward function could also be used in recent RL-based self-supervised methods for fine-tuning the image generative model, such techniques not only demand substantial computational resources but also lead to degraded image quality when trained with such a task task-specific reward [52]. To this end, propose novel training-free free approach that effectively avoids image quality degradation while maximizing orientation reward at test time.
In particular, given reward function R defined based on GroundingDINO and Aryent (which we will discuss in detail in Sec. 3.2) our goal is to find a latent sample x that maximizes the reward of its corresponding image, expressed as (FFo c)). For simplicity, we define the pullback of the reward function as R R Fo and use this notation throughout.
Why Based on a One-Step Generative Model? Despite extensive research on test-time reward- guided generation using pretrained diffusion models
we find that the main challenge stems from the multi-step nature of these models. Reward-guided generation requires computing rewards for the expected final output intermediate denoising steps and applying gradient ascent. However, in early stages, the expected output is too blurry to provide meaningful guidance, while in later stages-wher -when the output becomes clearer gradient updates have minimal effect. contrast, a one-step model generates a clear image directly from prior sample, enabling more effective guidance. Comparative results are presented Section
Our Technical Contribution. The gradient ascent approach for maximizing the future reward R can also be applied to the one-step model, using the following update rule on the latent x; at each iteration i:
x;-1 = x; vx()),
where 17 is step size. However, this gradient ascent in the latent space pose several challenges: (1)
the latent sample x may get stuck in local maxima [55] 56) before achieving the desired orientation
alignment, (2) the mode-seeking nature of gradient ascent can reduce sample diversity [57]. and
(3) x may deviate from the prior latent distribution N(0, I), resulting in unrealistic images [48] 58
Although the recent reward- guided noise optimization method (ReNO (22) employs norm- bosm-based
regularization 43 59] to enforce the latent to be close to the prior distribution, it still suffers from
local optima, leading to suboptimal orientation grounding results (see comparisons in Sec. 4 and
further analysis Appendix Aj.
Our key idea to address this is to reformulate the problem as sampling problem rather than an optimization problem. Specifically, we aim to sample from a target distribution q" that maximizes the expected reward, while ensuring remains close to the original latent distribution:
arg max Ex-p R [x)] aDr (p)
where a E RR is a constant that controls the regularization strength, and DXL (-1.)) is the Kullback- Leibler divergence [60]. This objective is closely related to those used in fine- Ame-tunnig--ased approaches for reward maximization [6] 48, 62). However, the key difference is that, while these methods define the target distribution q for the output images, we define it for the latent samples, setting q as the prior distribution A ((0 I). This formulation leads to our key contribution: a novel, simple, and effective sampling method based on Langevin dynamics, discussed in Sec. 3.3
Below, we first introduce our reward function designed for 3D orientation grounding (Sec. 3.2 and propose soward-guider Langevin dynamics to effectively sample from our target distribution 9* (See. B.3 Lastly, we introduce reward-adaptiv time rescaling speed up sampling convergence by incorporating time scheduling (Sec. 3.4)
3.2 Multi-Object Orientation Grounding Reward
To define our reward function R, we first detect the described objects the generated image using GrONndingDINO [54] with the provided object phrases W. yielding cropped object regions Crop(I, w:) for each phrase w.. We then use OrientAnything (0 to assess how well the orientations of the detected objects align with the specified grounding conditions Q. OrientAnything represents

---

object orientations as categorical probability distributions over one-degree bins for each Euler angle. Let (.).) denote the predicted distribution for the j-th angle. Following the convention from OrientAnything, we define the target distribution ( ) as a discretized Gaussian centered at the reference angle th.5. The reward function R is then computed as the negative KI. divergence between the predicted and target distributions, summed across all angles and all objects:
1 N jej
Σ E DRL (D, (Crop(I, w.)) [((oss).
RLL,
(3)
3.3 Reward-Guided Langevin Dynamics
We now introduce a method to effectively sample a latent x from our target distribution in Eq. To address the limitations of vanilla gradient ascent for reward maximization (as discussed in Sec. 3.1 we propose enhancing the exploration of the sampling space of x by injecting stochasticity, which is known help avoid local optima saddle points [55] [56]. In particular,
show that simulating
Langevin dynamics, in which the drift term is determined by our reward function (Sec. 3.2 can sample x that effectively aligns with the grounding orientation conditions.
Proposition 1. Reward-Guided Langevin Dynamics. Let q = (0, denote the prior distribution, R(x) be the pullback of differentiable reward function, and w, denote the standard Wiener process. As t + co, the stationary distribution of the following Langevin dynamics
X 1 vR(x))
dx.
dt + dw.
coincides with the optimal distribution of Eq.
Proof. See Appendix B
This demonstrates that simulating the Langevin stochastic differential equation (SDE) (Eq. 4) in the latent space ensures samples x are drawn from the target distribution q", balancing reward maximiza- tion with proximity to the prior distribution (Eq. 2). Using Fuler-Maruyama discretization 63 we express its discrete-time approximation as:
1 vR(x;) a
X;+1 R
x;
x;
81. + Vōlei,
2
where e; (0, I).
By introducing the substitutions 7 + 8t and 11 + 2. the above expression
(Eq. simplifies to the following intuitive update rule:
Xi+1 /1 - VR(x) + V5c..
(6)
Notably, this final update step (Eq. is surprisingly simple. Compared to the update step in standard gradient ascent (I, only the stochastic noise term and scaling factor additionally introduced, which require just single additional line of code. This update rule integrates exploration through noise while explicitly ensuring proximity prior distribution.
3.4 Reward-Adaptive Time Rescaling
While our reward-guided Langevin dynamics already enables effective sampling of x for 3D ori- entation grounding, we additionally introduce reward-adaptive time rescaling further accelerate convergence via time rescaling. In Leroy et al. [64). a time-rescaled SDE is introduced with a monitor function G that adaptively controls the step size, along with a correction drift term 1 (V)(R( to preserve the stationary distribution of the original SDE. By defining a new time variable r and our time-rescaled
the corresponding process x, = X with correction drift term version of reward-guided Langevin SDE in Eq. is expressed as:
dx, G((()))) (-1xx- + + dr (G(R(x))) (()((
where the original time increment is rescaled according to dt G(R((x,)) and dw,
5

---

Algorithm ORIGEN
Require:
(Prompt), W (Object phrase set), & (Ground- ing orientation set), Fo (One-step T21 model), (Re- ward), 9 (Monitor), M (# Steps), (Scale), y (Step size)
batent din.
I), R. inf I: Initialize xa (0,
datta dist.
for to
3:
R. = (((F(xx,,)) W.
b Reward Calc.
Ti G(R:)7
Timestep rescaling
Figure 2: Toy experiment results. Top: latent space samples (blue); bottom: data space samples (red). Gray dots show the original distribution without reward guid- ance. From left to right: (1) ground truth target distribution from 2 (2) results of
5:
6, ~ I((.I)
x vI -1--xx 7:x 1x
7on Ron R. & log (R.)) Update slep
+ Viiei
7:
R, > R. then
8:
R. = R., =X
9:
end if
ReNO [22), (3) results of ours with uniform time scaling, and (4) results of ours with reward-adaptive time rescaling.
end for
return c)
V (((((,,)) A detailed derivation and convergence analysis of Eq. are provided in Ap- pendix C Defining (xx) ) = ((R(xx)) we obtain the following time-rescaled update rule via Euler- Baler-Maruyama discretization:
X (x,,x, (x, (R(x;) + (xx 9(((x( (x;;)
(8)
Regarding the design the monitor function, existing work 664 suggests setting the step size inversely proportional to the squared norm of the drift coefficients in Eq. However, this approach requires computing the Hessian of the reward function for the correction term, which is computation- ally heavy. Alternativlly, we propose following simple, reward-adaptive monitor function:
((R(x)) mmin tanh
($max
8min )
where the hyperparmeters Smin Smax, and are set 3. $. and 0.3 our experiments, respectively. This function adaptively scales the step size by assigning smaller steps in high-reward and larger steps in low-reward, thereby improving convergence speed and accuracy. Please refer to Fig. 5 in Appendix that provides the visualization of our monitor function ((R(x)).
In Fig. we present a toy experiment demonstrating the effectiveness of our Langevin dynamics and reward- adaptivee time rescaling compared gradient ascent with regularization (ReNO 22 See Appendix D for details on the toy experiment setup. The top row shows the ground truth target latent distribution q" (leftmost) alongside latent samples generated by different methods, and the bottom row displays the corresponding data distributions. While ReNO [22) fails to accurately capture the target distribution due to its mode-seeking behavior (2nd column, as discussed in Sec. [.1Ip our method successfully aligns with it (3rd column), and time rescaling further accelerates convergence (4th column). Our sampling procedure is outlined in Alg. Note that setting G G(R(x)) 1 reverts the method to the uniformly reserlec form.
4 Experiments
4.1 Datasets
To the best of our knowledge, existing benchmark has been proposed to evaluate 3D orientation grounding in text-to-image generation, aside from user studies conducted by Cheng et al. [18). To address this, introduce text prompts, image, and bounding boxes.
based on the MS-COCO dataset 23 that consist of diverse
For a comparison with previous orientation -oriiatiionngruuiinng methods [88 17 that can condition only single object, we construct the ORIBENCH-Sing dataset. From MS-COCO validation set 23), we filter out (1) object classes for which orientation cannot be clearly defined (e.g.. objects with front-back symmetry) and (2) image captions lacking explicit object references. Since
ORIBENCH-Single.

---

Orientation
1-to-3
FreeDoM
ORIGEN (Ours)
"a with many painted decorations parked in devert."
L
"a car driving through very flooded street.
PLAS
Figure 3: Qualitative comparisons on OR-SgNCH-Single benchmark (Sec. 4.5). Compared to the existing orientation-to-image models 17] ORIGEN generates the most realistic images, which also best align with the grounding conditions in the leftmost column.
Orientation
DPS
MPGD
FreeDoM 35
ReNO
ORIGEN (Ours)
"a cut and a giruffe
Jahaffi
"a horse and a tv
Figure 4: Qualitative comparisons on ORIBENCH-MUIE benchmark (Sec. (.5). Compared to the guided-generation methods [32 3 35 22). ORIGEN generates the most realistic images, which also best align with the grounding conditions the leftmost column.
the current orientation-to-mnan image generation model [8] is only capable of controlling the front 180 range azimuths, we further filter the samples to only include those within this range. This procedure yields 252 text-image pairs covering 25 distinct object classes. Using the provided bounding boxes, we cropped the foreground objects and fed them into OrientAnything 200. an orientation estimation model, to obtain pseudo-GT grounding orientations. Building upon this dataset, ORIBENCH-Single was constructed by mix-matching the image captions and grounding orientations, ultimately forming dataset consisting of 25 object classes, each with 40 samples, totaling IK samples (See Appendix to check object classes we used).
ORIBENCH-Multi For the comparison of complex scenario with the grounding orientation of multiple objects, we construct ORIBENCH-MAN Single. Since base dataset (33 lacks samples composed solely of objects with clear orientation, we mix-mateh object classes and grounding orientations from the 252 non-overlapping text-image pairs in ORIBENCH-Single forming dataset consisting of 371 samples, each containing varying number of objects. We annotated prompts by concatenating individual object phrases (e.g., "a cat, and dog.").
following an approach to that in ORIBENCH-
4.2 Evaluation Metrics
We compare ORIGEN'S performance from two perspectives: (1) Orientation Alignment and (2) Text-to- Image Alignment. We measure orientation alignment using two metrics: 1) Acc. @22.5".

---

Table 1: Quantitative comparisons on 3D orientation grounded image generation. Best and second-bes best results are highlighted in bold and underlined, respectively. ORIGEN" denotes ours without soward-adaptive time rescaling.
ORIBENCH Single
ORIBENCH Mulii
dd Model 22..0
Orientation Aligomenn
Text-to-lmage Aligument
Orieatation Aligunacut
Text-to Image Aligament
4
CLI VQA
PickSorre
Acc. 022.5222
Brr.
VQA
PickScore
Fine-tuned Oricntaiionto--naae Model
Zero-Las 1
0,499
59.08
0.272
0.633
0.213
C3DW
0,426
64.77
0.439
(2) Guided Generation with Step
DPS X34 MPGD
28. 16
0.246
0.477 0.532
38.28
0.285
0.743
0.231
0.689
27.62
0.246
0.661
0.222 0.225
35.03
0.281
0.739
0.232
FreeDoM] A5]
0
20.90
0.259
0.728
0.591
31.77
0.279
0.783
0.227
(3) Guided- Generation Methods with One Steep Model
RcNO 28
0,796
20.56
0.247
0.663
0,212 0.224
0.478
45.95
0.277
0.756
0,223
ORIGEN (Ours)
0.854
18.28
0.265
0.752
0.477
29.7
0.287
(1904
0.225
ORIGEN (Ours)
0,871
17.41
0.203
0,735
0.224
0,692
28.4
0.387
0.507
0.225
the angular accuracy within a tolerance of +:22.5 and 2) the absolute error on azimuth angle between the predicted and grounding object orientations, following Wang al. [20]. For evaluation, we use OrientAnything [20] to predict the 3D orientation from the generated images. Since its predictions may not be perfect, we also conduct a user study in Sec. 4.6 to validate the results. Along with grounding accuracy, we also evaluate text-to-image alignment using CLIP Score [65], VQA-Score [66], and PickScore [67].
4.3 Baselines
We compare ORIGEN with three types of baselines: (1) training-based orientation to-mmae generation
methods 18 17 (2) training-free guided generation methods [35] 22 for the multi-step generation, and (3) training-fres free guided generation methods for the one-step generation. For (1), we include Continuous 3D Words (C3DW) [8] and Zero-I-to-3 [7). following the baselines used in the most recent work in this field [18]. For (2), we consider DPS (32) MPGD (33], and FreeDoM (35), which update the intermediate samples at each step of the multi-step sampling process based on the expected reward computed the estimated clean image. Finally, for (3), compare ORIGEN with ReNO (22). which optimizes the initial latent through vanilla gradient ascent using one-step models. Furthermore, to evaluate the effectiveness of our reward adaptive time rescaling, perform additional comparison with our method variant without this component, denoted as ORIGEN".
4.4 Implementation Details
[2T] as the one-step generative model for both ORIGEN and ReNO 22 while all multi-step guided generation baselines (DPS (32), MPGD [3), and FreeDoM 335 are implemented using FLUX-Dev 21 as the multi-step generative model. Except for the training- based methods (C3DW [8] and 7ero--lto-3 [17), we match the number of function evaluations defiee as the number of iterations in our method and the denoising steps in multi-step generative models) to 50 for fair comparison across all methods. All experiments were conducted on single NVIDIA 48GB VRAM A6000 GPU, Further implementation details are provided in Appendix
We use
4.5 Results
ORIBENCH-Single. In the left part of Tab. we show our quantitative comparison results on the ORIgENCH-Single benchmark. ORIGEN significantly outperforms all the baselines in orientation alignment, showing comparable performance in text-to-image alignment. Our qualitative comparisons are also shown Fig. 3 demonstrating that ORIGEN generates high-quality images that align with the orientation conditions and input text prompts. Note that C3DW [18] is trained on synthetic data (i.e., multi-view renderings of a 3D object) to learn orientation- tittattiioott-imaae to-image generation. Thus, it has limited generalizability to real-world images and the output images lack realism. Zero- 1-to-3 17 is also trained on single-object images but without backgrounds, requiring additional
We perform comparisons only on azimuth angle, as existing methods [18 17 do not support the control over polar and rotation angles. Note that our results for all azimuth, polar, and rotation angles are provided in Appendix G.]
8

---

Table 2: User Study Results. 3D orientation -orruiddd text-to- -image generation results of ORIGEN was preferred by 58.18% of the participants on Amazon Mechanical Turk [69] significantly outper- forming the baselines 18
Method
Zero-1 1-to to-3 17
C3DW 18]
ORIGEN (Ours)
User Preferences f (%)
20.58
21.24
58.18
background image composition (also used in the evaluation of C3DW (8] that may introduce unnatrual artifacts. The existing methods on guided generation methods also achieve suboptimal results compared to ORIGEN. In particular, all training- free multi-step guidance methods, including DPS [32). MPGD [33,, and FreeDoM [35], perform poorly in orientation grounding. This is because object orientation control relies modifying low-frequency image structures, which are primarily formed during early sampling stages, where multi-step diffusion models produce noisy and blurry outputs (68] (as discussed in Sec. 3.I. ReNO [2) also achieves suboptimal results compared ours, as it performs latent optimization based on vanilla gradient ascent which is prone to local optima (as discussed in Sec. P... Overall, our method achieves the best results both with and without time rescaling, demonstrating its ability to effectively maximize rewards while avoiding over-optimization.
ORIBENCH-Multi. We additionally show the quantitative comparison results on the ORIBENCH-Muid benchmark. Since no fine-tuned methods ((1) in Tab [ are capable of multi- object orientation grounding, we assess our method only with guided generation methods ((2), (3) in Tab. T. Our quantitative results are presented in the right of Tab. [ and qualitative examples are provided in Fig. 4 As shown, ORIGEN consistently outperforms all guided generation baselines under the same reward function. These results demonstrate that our object-agnostic reward design, combined with the proposed method, enables robust and generalizable orientation grounding even in complex multi-object scenarios.
We provide additional results for orientation grounding task (1) under more diverse orientations, and (2) for four primitive views (front, left, right, back) to enable comparisons with rext-to-image models, where the orientation condition is included as an input text prompt. Please refer to Appendix G
Additional Results.
4.6 User Study
Our previous quantitative evaluations were performed by comparing the grounding orientations and orientations estimated from the generated images using OrientAnything [20]. While its orientation estimation performance highly robust (as seen in all of our qualitative results), we additionally conduct user study to further validate the effectiveness of our method based on human evaluation performed by 100 participants on Amazon Mechanical Turk [69]. Each participant was presented with the grounding orientation, the input prompt, and the images generated by (1) Zero-I-to-3 (17), (2) C3DW [88, and (3) ORIGEN. Since ORIGEN outperformed all guided-generation -enereattennn methods with same reward function design in quantitative comparison, they were excluded in the user study. Then, participants were asked to select the image that best matches both the grounding orientations and the input text prompt- directly following the user study settings in C3DW (18]. In Tab. 2 ORIGeN was preferred by 18%18% of the participants, clearly outperforming the baselines. For more details of this user study, refer to Appendix H
4.7 Computation Time
In our experiments, we set the number of iterations in our method to match the denoising steps in multi-step generative models (e.g., 50), resulting in comparable computation time approximately 52.7 seconds per image. detailed comparison is provided in Appendix [ Notably, for cases achieving angular accuracy within tolerance of =22.5 (Tab. 3. the average number of iterations to reach this threshold was 12.8, corresponding to an average of 13.5 seconds. Our approach can also serve as a post-refinement method for existing models, potentially further reducing computation time.
5 Conclusion
We presented ORIGEN, the first 3D orientation grounding method for text-to-image generation across multiple objects and categories. To enable test-time guidance with pretrained discriminator, we introduced Langevin-based sampling approach with reward-adaptive time rescaling for faster convergence. Experiments showed that our method outperforms fine-tuning teie-ttning-baseee based baselines and uniquely supports conditioning on multiple open- apen-vocabulary objects.
9

---

Limitations and Societal Impact. Since our method relies on pretrained discriminator, its performance is inherently bounded by the quality of that discriminator though in our case, still achieved state-of-the-art results by significant margin. As generative Al technique, our method may misused to produce inappropriate or harmful content, underscoring the importance of responsible development and deployment.
Acknowledgement
This work was supported by the NRF of Korea (RS-2023-002099722)) ITР grants (RS-2022- 11220594, RS-2023-00227599, RS-2024-003998177 RS-2025-25441313 RS-2025-25443318 RS- 2025-02653 13); the National Program for Excellence in SW, supervised by the IITP; and the Technology Innovation Program (RS-2025-02317326)) all funded by the Korean government (MSIT and MOTIE), as well as by the DRB-KAIST TeetchTheFuture Research Center.
References
[1] Phillip Y Leee and Minhyuk Sung. ReGround: Improving textual and spatial grounding at cost. In ECCV, 2024.
Phillip Y. Lee, Taehoon Yoon, and Minhyuk Sung. GrounDiT: Grounding diffusion transformers via noisy patch transplantation. In NeurlPS, 2024.
[3] Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jac Lee. GLIGEN: Open-set grounded text-to-image generation. CVPR, 2023.
[4] Arpit Bansal, Mong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Universal guidance for diffusion s models. In CVPR, 2023.
[5] Wan-Duo Kurt Ma, Avisek Lahiri, John P Lewis, Thomas Leung. and W Bastiaan Kleijn. Directed diffusion: Direct control of object placement through attention guidance. In AAAI, 2024.
[6] Omri Avrahami, Thomas Hayes, Oran Gafni, Sonal Gupta, Yaniv Taigman, Devi Parikh, Dani Lischinski, Ohad Fried, and Xi Yin. SpaText: Spatio-textual representation for controllable image generation. In CVPR, 2023.
[7] Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, al. cDiff-I: Text-to-image diffusion models with ensemble of expert denoisers. arXiv preprint #xXi:22/1.01322 2022.
[8] Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. MultiDiffusion: Fusing diffusion paths for controlled image generation. In /CML, 2023.
[9] Guillaume Couairon, Marlene Careil, Matthicu Cord, Stéphane Lathuilicre, and Jakob Verbeek. Zero-shot spatial layout conditioning for text-to-image diffusion models. In ICCV, 2023.
[10] Yinlin Hu, Joachim Hugonot, Pascal Fua, and Mathieu Salzmann. Segmentation-driven 6D object pose estimation. In CVPR, 2019.
[11] Bugra Tekin, Sudipta N Sinha, and Pascal Fua. Real-time seamless single shot 6D object pose prediction. In CVPR, 2018
[12] Fric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, and Carsten Rother. Learning 6D object pose estimation using 3D object coordinates. In ECCV, 2014.
[13] He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, and Lconidas J Guibas. Normal- ized object coordinate space for category-Iewe 6D object pose and size estimation. In CVPR, 2019.
[14] Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martin-Martin, Cewu Lu, Li Fei-Fei, and Silvio Savarese. DenseFusion: 6D object pose estimation by iterative dense fusion. In CVPR, 2019.
[15] Tomás Hodañ, Jif Matas, and Štěpán Obdržálek. On evaluation of 6D object pose estimation. In ECCV, 2016.
[16] Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner, Manuel Brucker, and Rudolph Triebel. Implicit 3D orientation learning for 6D object detection from rgb images. In ECCV, 2018.
10

---

[17] Ruoshi Liu, Rundi Wu, Basile Van Hoorick. Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-I-to-3: Zero-shot one image to 3D object. In ICCV, 2023.
[18] Ta- Ying Cheng. Matheus Gadelha, Thibault Groueix, Matthew Fisher, Radomir Mech, Andrew Markham, and Niki Trigoni. Learning continuous words for text-to-image generation. CVPR, 2024.
[19] Yuzhong Huang, Zhong Li, Zhang Chen, Zhiyuan Ren, Guosheng Lin, Fred Morstatter, and Yi Xu. OrientDream: Streamlining text-to-3D generation with explicit oricntation control. arXiv preprint arXiv:2406. 10000, 2024.
[20] Zehan Wang, Ziang Zhang, Tianyu Pang. Chao Du, Hengshuang Zhao, and Zhou Zhao. Orient Anything: Learning robust object orientation estimation from rendering 3D models. arXiv preprint 2024.
[21] Black Forest Labs. http::///gttib..cc////addddrrrer
2024.
[22] Luca Eyring. Shyamgopal Karthik, Karsten Roth, Alexey Dosovitskiy, and Zeynep Akata, ReNO: Enhanc- ing one-step lext-to-image models through reward-based noise optimization. NewrIPS, 2024.
[23] Tsung- Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft COCO: Common objects in context. In ECCV, 2014.
[24] Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Frédo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In CVPR, 2024.
[25] Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion distillation. In ECCV, 2024.
[26] James Burgess, Kuan-Chich Wang, and Serena Yeung-Levy. Viewpoint textual inversion: Discovering scene representations and 3D view control in 2d diffusion models. In ECCV, 2024.
[27] Nupur Kumari, Grace Su, Richard Zhang, Taesung Park, Eli Shechtman, and Jun- Yan Zhu. Customizing text-to-image diffusion with object viewpoint control. SIGGRAPH Asia, 2024.
[28] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In ICCV, 2023.
[29] Gyeongnyeon Kim, Wooscok Jang, Gyuscong Lee, Susung Hong, Junyoung Sco, and Scungryong Kim. DAG: Depth-aware guidance with denoising diffusion probabilistic models. arXiv preprint 28886, 2022.
[30] Shariq Farooq Bhat, Niloy Mitra, and Peter Wonka. LooseControl: Lifting controlnet for generalized depth conditioning. In SIGGRAPH, 2024.
[31] AbdeIrahman Eldesokey and Peter Wonka. Build-A-Scene: Interactive 3d layout control for diffusion-based
image generation. In ICLR, 2025.
[32] Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Mare Louis Klasky, and Jong Chul Ye. Diffusion posterior sampling for general noisy inverse problems. In ICLR, 2023.
[33] Yutong He. Naoki Murata, Chieh-Hssin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J Zico Kolter, Ruslan Salakhutdinov, and Stefano Ermon. Manifold preserving guided diffusion. In ICLR, 2024.
[34] Jiaming Song, Arash Vahdat, Morteza Mardani, and Jan Kautz. Pscudoinverse-guuide diffusion models for inverse problems. In ICLR, 2023.
[35] Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, and Jian Zhang. FreeDoM: Training- free energy- guided conditional diffusion model. ICCV, 2023.
[36] Charles M. Stein. Estimation the mean a multivariate normal distribution. The Annals of Statistics, (6):1135-1151, 1981.
[37] Yang Song. Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency Models. In JCML, 2023.
[38] Dongjun Kim, Chich- Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, and Stefano Ermon. Consistency Trajectory Models: Learning probability flow ODE trajectory of diffusion. In ICLR, 2024.

---

[39] Shanchuan Lin, Anran Wang, and Xiao Yang. SDXL-Lightning Progressive adversarial diffusion distillation. arXiv preprint arXiv:2402 1992292...999,, 2024.
[40] Weijian Luo, Zemin Huang. Zhengyang Geng, J. Zico Kolter, and Guo jun Qi. One-step diffusion distillation through score implicit matching. In NeurIPS, 2024.
[41] Xingehao Liu, Chengyue Gong, and Qiang Liu. Flow Straight and Fast: Learning generate and transfer data with rectified flow. In ICLR, 2023.
[42] Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, al. InstaFlow: One step is enough for high-quality diffusion-based text-to-image generation. In ICLR, 2024.
[43] Heli Ben-Hamu, Omri Puny, Itai Gat, Brian Karrer, Uriel Singer, and Yaron Lipman. D-Flow: Differentiat- ing through flows for controlled generation. In ICML, 2024.
[44] Bram Wallace, Akash Gokul, Stefano Ermon, and Nikhil Naik. End-to-end diffusion latent optimization improves classifier guidance. ICCV, 2023.
[45] Xiefan Guo, Jinlin Liu, Miaomiao Cui, Jiankai Li, Hongyu Yang. and Di Huang. InitNO: Boosting text-to-image diffusion models via initial noise optimization. CVPR, 2024.
[46] Dvir Samuel, Rami Ben-Ari, Nir Darshan, Haggai Maron, and Gal Chechik. Norm- Norm-guided latent space exploration for text-to-image generation. In NeurIPS, 2023.
[47] Ye. Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. IP-Adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint arXiv:2308.0672) 2023.
[48] Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mo- hammad Ghavamzadeh, Kangwook Lee, and Kimin Lec. DPOK: Reinforcement learning for fine-ture tee-tuning text-to-imas image diffusion models. In NewrlPS, 2023.
[49] Fanyue Wei, Wei Zeng, Zhenyang Li. Dawei Yin, Lixin Duan, and Wen Li. Powerful and flexible: Personalized text-to-image generation via reinforcement learning. In ECCV, 2024.
[50] Mihir Prabhudesai, Anirudh Goyal, Deepak Pathak, and Katerina Fragkiadaki. Aligning text-to-image diffusion models with reward backpropagation. arXiv preprint arXiv:2310.03739, 2023.
[51] Weijian Luo. Diff-Instruct++: Training one-step text-to-image generator model to align with human preferences. arXiv preprint 2rXiv:2410. 18881, 2024.
[52] Sunwoo Kim, Minkyu Kim, and Dongmin Park. Test-time alignment of diffusion models without reward over-optimization. In ICIRR, 2025.
[53] Yaron Lipman, Ricky TQ Chen, Heli generative modeling. In ICLR, 2023.
Maximilian Nickel, and Matt Le. Flow matching for
[54] Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang. Chunyuan Li, Jianwei Yang. Hang Su, et al. Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection. In ECCV, 2024.
[55] Max Welling and Yee W Teh. Bayesian learning via stochastic gradient langevin dynamics. In ICML, 2011.
[56] Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky. Non-convex learning via stochastic gradient langevin dynamics: a nonasymplotic analysis. Conference on Learning Theory, 2017.
[57] Rohit Jena, Ali Taghibakhshi, Sahil Jain, Gerald Shen, Nima Tajbakhsh, and Arash Vahdat. Elucidating optimal reward-diversity tradcoffs in text-to-image diffusion models. arXiv preprint orXiv:2409.0649) 2024.
[58] Masatoshi Uchara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Loe Diamant, Alex M Tseng, Tommaso Biancalani, and Sergey Levine Fine-tuning of continuous-time diffusion models as entropy-regularizzed control. arXiv preprint arXiv:2402. 15194, 2024.
[59] Dvir Samuel, Rami Ben-Ari, Simon Raviv, Nir Darshan, and Gal Chechik. Generating images of rare concepts using trantrd diffusion models. In AAAI, 2024.
[60] Solomon Kullback and Richard A. Leibler. On information and sufficiency. Annals of Mathematical Statistics, 22(1):79-86 1951.
12

---

[61] Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct Preference Optimization: Your language model is secretly reward model. In NewrIPS, 2023.
[62] Hao Lang, Fei Huang, and Yongbin Li. Fine-tuning language models with reward learning on policy. arXiv preprint Xiv:24003 19279, 2024.
[63] Francesco Gianfelici. Numerical solutions of stochastic differential equations (kloeden, pk and platen, e.; 2008)[book reviews]. IEEE Transactions on Neural Networks, 19(11):190-1199, 2008.
[64] Alix Leroy. Benedict Leimkuhler. Jonas Latz, and Desmond J Higham. Adaptive stepsize algorithms for langevin dynamics. SIAM Journal on Scientific Computing, 46(6))43544-33398 2024.
[65] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: reference-free evaluation metric for image captioning. In EMNLP, R. 2021.
[66] Zhiqiu Lin, Deepak Pathak. Baiqi Li, Jiayao Li. Xide Xia. Graham Neubig, Pengchuan Zhang, and Deva Ramanan. Evaluating text-to-visual generation with image-to-text generation. In ECCV, 2024.
[67]
Yuval Kirstain, Adam Polyak, Uriel Singer. Shahbuland Matiana, Joe Penna, and Omer Levy. Pick-a-pic: An open dataset user preferences for text-to-image generation. NeurIPS, 2023.
[68] Jooyoung Choi, Jungheom Lee, Chachun Shin, Sungwon Kim, Hyunwoo Kim, and Sungroh Yoon. Perception prioritized training of diffusion models. In CVPR, 2022.
[69] Michael Buhrmester. Tracy Kwang, and Samuel D Gosling. Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality data? Perspectives on Psychological Science, 6(1):3-5, 2011.
[70] S. Kirkpatrick, C. D. Gelatt, and P. Vecchi. Optimization by simulated annealing. Science, 220(4598); 671-680, 1983. doi: 10. 10.1126/scence. 220.4598.677
[71] Bernt Øksendal. Stochastic Differential Equations: An Introduction with Applications. Springer- Verlag Berlin Heidelberg, 6 edition, 2003. ISBN 978-3-540-04758-2. doi: 10.1007/978-3--42- --2-44444---4-- 14394-6.
[72] Grigorios A. Pavliotis. Stochastic Processes and Applications: Diffusioo Processes, the Fokker-Planck and Langevin Equations, volume 60 of Texts in Applied Mathematics. Springer, 2014. ISBN 978- doi: 10. 1.110007978---4999111 1323-77
[73] HuggingFace. OpenAI-CLIP, 2022.
[74] Haotian Liu, Chunyuan Li. Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In NeurlPS, 2023.
[75] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan- Yen Lo, Piotr Dollar, and Ross Girshick. Segment Anything. In ICCV, 2023.
[76] Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Nacjin Kong, Harshith Goka, Kiwoong Park, and Victor Lempitsky. Resolution-rohust large mask inpainting with fourier convolutions. WACV, 2022.
[77] Kevin Black, Michael Janner, Yilun Du llya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning. In ICLR, 2024.
[78] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsami, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of annotated 3d objects. In CVPR, 2023.
[79] Tiange Luo, Chris Rockwell, Honglak Lee, and Justin Johnson. Scalable 3d captioning with pretrained models. In NeurlPS, 2023.
[80] Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Rohin Rombach, and Varun Jampani. Sv3d: Novel multi-view synthesis and 3d generation from single image using latent video diffusion. In ECCVV pages 439-457. Springer, 2024.
[81] Zehuan Huang. Yuan-Chen Guo, Haoran Wang. Ran Yi. Lizhuang Ma, Yan-Pei Cao, and Lu Sheng. Mv-adapter: Multi-vies view consistent image generation made easy. arXiv preprint arXiv:2412.0366 2024.
[82] Eslam Mohamed Bakr, Pengzhan Sun, Xiaoqian Shen, Faizan Farooq Khan, Li Erra Li, and Mohamed Elhoseiny. Hrs-bench: Holistic, reliable and scalable benchmark for text-to-image models. In ICCV, 2023.
[83] Xiuquan Hou, Meiqin Liu, Senlin Zhang, Ping Wei, and Badong Chen. Salience detr: Enhancing detection transformer with hierarchical salience filtering refinement. In CVPR, 2024.
[84] Lihe Yang. Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. In NewrIPS, 2024. 2024.
13

---

NeurIPS Paper Checklist
The checklist is designed encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit.
Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:
You should answer [Yes], [No], or [NA].
[NA] means either that the question Not Applicable for that particular paper or the relevant information is Not Available.
Please provide short (1-2 sentence) justification right after your answer (even for NA).
The checklist answers are an integral part of your paper submission. They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include (after eventual revisions) with the final version of your paper, and its final version will published with the paper.
The reviewers of your paper will be asked use the checklist as one of the factors their evaluation. While "[Yes] is generally preferable to "[No]" ", it is perfectly acceptable to answer "[No] providedd proper justification is given (e.g.. "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). In general, answering binary way, we
"[No] or "[NA]" is not grounds for rejection. While the questions are phrased acknowledge that the true answer is often more nuanced, please just use your best judgment and write justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] to a question, in the justification please point to the section(s) where related material for the question can be found.
IMPORTANT, please:
Delete this instruction block, but keep the section heading "NeurIPS Paper Checklist",
Keep the checklist subsection headings, questions/answers and guidelines below.
Do not modify the questions and only use the provided macros for your answers.
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results be expected to generalize other settings.
is fine include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
14

---

Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, c.g., if the approach was only tested few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
Justification:
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
15

---

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution
specific model and empirical evaluation, may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, described in supplemental material?
Answer: [No]
Justification: Our two main datasets CONeets-RRBENNHH-SSSS Single and ORIBENCH- (in Sec. [..] carefully curated subsets of the MS-COCO validation set [23], specifically
selected to support fine-grained evaluation of 3D orientation controllability in image gener- ation. While these datasets are not currently released, they are constructed from publicly available datas using a reproducible filtering protocol, which we plan to release in future revisions. The code is also under preparation for public release.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.co cc/
sww.uubiiissioooooccccooo for more details.
While encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https //nips.
for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
16

---

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core the paper to a level of detail that is necessary appreciate the results and make sense of them.
The full details can be provided either with the code, appendix, as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [No]
Justification: Due to time and GPU constraints, we were unable to conduct a thorough statistical significance analysis prior submission. However, we plan to include appropriate statistical evaluations (c.g., standard deviation, confidence intervals) and additional ablation studies in future revision.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results accompanied by enror bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
Question: For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
17

---

Answer: [Yes]
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code ethics
Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics httppp///eerrpp....ppppp/hhhhhha
Question:
Answer: [Yes]
Justification:
Guidelines:
The answer NA means that the authors have not reviewed the NeurlPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g.. there a special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification:
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (c.g., deployment of technologics that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional unintentional) misuse of the technology.
there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
18

---

Question: Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Answer: [NA]
Justification: Our work builds on pretrained models and does not involve the release of new high-risk models or datasets. Therefore, specific safeguards were not applicable in this context.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification:
Guidelines:
The answer NA means that the paper does existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URI.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors encouraged to reach out to the asset's 'ss creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: Although introduce new benchmark datasets (ORIBENCH-Single and Multi), they are not publicly released at this stage. As such, they are not considered released assets for the purposes of this checklist item.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/codeemoodel as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
19

---

The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [Yes]
Justification:
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then much detail as possible should be included in the main paper.
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/teview based the requirements of your country or institution) were obtained?
Answer: [Yes]
Justification:
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMSs if it is important, original, or non-standard component of the core methods this research? Note that the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification: Our work does not use any large language models as part of the core methodol- ogy.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer LLM policy for what should or should not be described.
20

---

Appendix
A Analysis on Norm-Based Regularization
In this section, we analyze the limitations of norm-based regularization in reward-guided noise optimization by highlighting its mode-seeking property, which can lead to overoptimization and convergence to local maxima. Consider the following reward maximization process with norm-based regularization 22 46]:
X++1 = X + 7/ VR(x)) + m2 log ([]x(()))))),
(10)
where = o Fa is the pullback the reward function R and y(1 [I) represents the probability density of x" distribution.
We view both R(-) and v(Il []) components of single reward, allowing to rewrite the update as
X₁ mn vR(x,) 7/2 v [og
X--
x, + 7h VR(xx) 7/2 7 (d 1)log |x,11
|x,
Xx + M (x(x)) 7/2 Vx(())
x, V
(11)
where (xx) = 1n R(x) + 7/2 1 (x). This optimization process an Fuler discretization (with 5t = 1) of the ODE
dx
(T((x),
(12)
dt
which represents deterministic gradient flow, whose stationary measure is weighted sum of Dirac deltas located at the maximizers of (xx) = . (x()) + 7/2 (x)
However, illustrated in Fig. in the main paper, this deterministic gradient ascent does not directly prevent the iterates from deviating substantially from the original latent distribution. Also, the process may collapse to local maxima-ever even ones where the reward is not sufficiently high [70]. Our empricial observations indicate that this approach is ineffective for our application, presumably because the reward function defined over the latent exhibits many local maxima, causing the deterministic ascent to converge to suboptimal solutions.
B Proofs
Proof of Proposition Recall the standard result from overdamped Langevin dynamics: if x, evolves according to the SDE:
dx,
2 v log q" (x:)dt dw,,
(13)
then (x) is the unique stationary distribution. Using Eq. 2 in the main paper, we obtain
V llog
2 + vi((x))
(14)
2
2a
Integrating this with respect gives
R(xx
9" (x) x g(x) exp
exp
(15)
21

---

where (xx) is standard Gaussian distribution.
Finally, following existing approach [6], easily arrive at
q"
arg max Ex-x - (pl())
(16)
which matches the expression presented in Eq. 2 in the main paper.
C Analysis on Reward-Adaptive Time-Rescaled SDE
In this section, we analyze the effect of a position-dependent step size on reward-guided Langevin dynamics. We first illustrate why naive time-rescaling approach fails to preserve the desired stationary distribution, and how to fix it with an additional correction term, eventually leading to Eq. [ in the main paper. Our derivation follows the approach of Leroy al. [64]
C.1 Non-Convergence of Direct Time-Rescale SDE Consider the following SDE:
dx: b(xx)))))))))))) + (xx)) dwe.
(17)
with drift b(x,) and diffusion coefficient (xx).. Its probability density p(x, 1) evolves according to the Fokker-Planee equation [T]:
i
1 (o t)]
σt o(x, - (xx()x))(xx t)]
(18)
Thus, the stationary distribution must lie in the kernel of the corresponding Tokker-Plande operator J 72
L (') = -V ((x))
+ 1 v (x p(x)
(19)
Now, consider the reward-guided Langevin SDE in Eq. 4 in the main paper:
dx. = (-1 (-2x0 2 + vř(xx)) dt + dw,,
(20)
which has the stationary distribution q² (x) given by Eq. in the main paper. By comparing with the general form, identify:
1 b(x) - 1 VR(x), o(x) =
(21)
Hence,
(x) = - |(---- x vR(x)) (x)) + [/((0(
0.
(22)
Next, we introduce monitor function (xx) 0 and define new time variable by
dt = 9 (xx()) dr.
(23)
Defining the time-rescaled process x, x((r), we rewrite the SDE as
dx, = G(x,) (-1xx, + vπ((x) dr + v dW,.
(24)
In this rescaled SDE, the new drift and diffusion coefficients are
(-3x+ vR(x)). o(x) (g(x).
1
a(x) = ((x)
(25)
22

---

Table Convergence speed to the desired suc- cess criterion. ORIGEN" denotes ours without reward-adaptive time rescaling.
Method
ORIGEN
ORIGEN
Mcan NFE
14.2
12.8
Inforence time (5)
14.9
Applying the corresponding Fokker operator A to o" (x), we obtain
(x) = - [(xX) xx+ VR(x) 9
(x) (x)
1 v ((x)) (x)
[a(x) (x)
+ v (x) (x))
v [a(x)
2
v [()())[(x))
2
* 0,
(26)
where we used the fact that ((x) (x) = 2a(x) (x), which follows from the identity
V Vlog (x) + 2nj VR(x). Therefore, (xx is not annihilated by L implying that the
time-rescaled SDE does not converge to the desired target distribution Eq. 2 in the main paper.
C.2 Reseales SDE with Correction Drift Term
Convergence guarantee to original stationary distribution. Following previous work [64] we can add a correction drift term V"(x,) Eq. 24 to ensure that the rescaled process converges to the same stationary distribution as the original SDE. The modified SDE becomes
dx, (Xx+)
+ dr + VV(,))rr (g(x,)dW)
(27)
The corresponding Fokker- -lanckk operator 4 for this corrected time-rescaled SDE now annihilates the original stationary distribution 9 (x)):
Loon" (x) - [9(8)
+ 1 vR(x) (x) (x))) 1 v2 (()))(())
2
2 v. [v)()) - v. ('))
= 0.
(28)
Consequently gº (X) remains in the kernel of the b... showing that this corrected time-rescaled SDE preserves the original invariant distribution.
Convergenee speed time rescaling approach. To verify the effectiveness of our time-rescaling approach, we analyzed the average number of iterations required to reach the desired success criterion during reward- -wwrrd-guided Langevin dynamics on ORIBENCH-Sinee -ingll dataset. As shown in Tab, 3 ORIGEN requires average of 14.2 iterations to reach the desired reward level, whereas ORIGEN reduced this to 12.8 iterations, demonstrating improved convergence speed without image quality degradation. Notably, the computational overhead introduced by adaptive rescaling is negligible, as the term V log G (R;) in Alg. in the main paper can be efficiently computed via the chain rule, using the precomputed reward gradient VR,
D Setup for the Toy Experiment
We train a rectified flow model on domain, where the source distribution A((0 I) and the target distribution is a mixture of two ( and N( ozI), with μi
23

---

Class Number
Class Name
Airplane
2
Bear
Bench
4
Bicycle
5
Bird
6
Boat
7
Bus
Car
Cat
10
Chair
11
Cow
12
Dog
13
Elephant
14
Giraffe
15
Horse
Laptop
17
Motorcycle
Person
19
Sheep
20
Teddy bear
21
Toilet
22
Train
23
Truck
Tv
25
Zebra
Table 4: Selected object classes in our dataset.
(4,0)T, N22 (-4.0) 01 0.3, and 02 0.9. The velocity prediction network consists of four hidden MLP layers of width 128. We first train an initial model and distill it twice, resulting in 3-rectified flow model. The reward function is defined as
(_(x 4)2 (=x
y² + 0.1 exp
(- (x [x y²
R(x,v) v) exp
1.
(29)
In Fig. 2 in the main paper, all methods use the same total number of sampling steps.
E Selected Classes
When constructing the ORIBENCH benchmark, we selected a total of 25 classes from the 80 object classes in MS-COCO validation set (23). We excluded classes for which distinguishing the front and back difficult or where defining a canonical orientation is ambiguous. The remaining 25 classes chosen based their clear orientation cues. The detailed list of selected classes is provided in Tab.
Implementation Details
Following the convention of OrientAnything 200, we set the standard deviation hyperparameters
for the azimuth, polar, and rotation distributions to 20, 2, and 1, respectively, to transform discrete
angles into the orientation probability distribution II. For image quality evaluation, we utilized
the official implementation of VQA-Score (66). and assessed CLIP Score [5] VQA-Score [66].
and Pickscore 67 using OpenAl's CLIP-VIT-L-14-33 77 1.53V-v1.5-13b 74] and Pickscore-
vl 67 models, respectively.

---

1.0
s(R(x))
-9.0
-75
-6.0
-45
-3.0
-15 0.0
R00
Figure 5: Plot of our monitor function G ((((x)).
one-step T2I generative model and OrientAnything (ViT-L) 20 to measure the Orientation Grounding Reward, as detailed in Sec. 3.2 For all experiments, set ry 0.8 in Alg. [ and used y 0.3 for ORIBENCH--Sngg and 7 0.2 for ORIBENCH-Mult as this configuration provides a favorable balance between image quality and computational cost when evaluating with 50 NFEs. As described in Sec. 3.4 we additionally
About ORIGEN. We employed FLUX-Schnell 21
visualize our monitor function (xR(x)) from Eq. 9 in Fig. 5 which demonstrates how the function adaptively scales the step size- assigning smaller steps in high-reward regions and larger steps in low-reward regions-thereby improving convergence speed.
About Guided Generation Methods. For ReNO [22] we employed FLUX-LcUUni (1 as the base one-step text-to-image generative model and OrientAnything (ViT-L) (0 as in ours. For multi-step guidance methods, we used FLUX-Dev (1 multi-step text-to-image generative model. We set the gradient weight hyperparameter to 0.3 for ORIBENCH-SINE -iniglee and 0.2 for ORIBENCH-Mult for all methods. In the case ReNO [22] we use norm-ban basedd regularization weight of 0.01. To ensure
fair comparison, we matched the number of function evaluations (NFEs) for all training-fiee free one-step, multi-step guidance methods to 50.
About Zero-1-to-3 [7). For the Zero-1-to-3 17 baseline, we generated the base image using FLUX-Schnell (512x512) with steps and encouraged the model to generate front-facing objects by adding the "facing front" prompt. The model was then conditioned on the target azimuth while keeping the polar angle fixed (0 to generate novel foreground views, which later composited with the background. The foreground was segmented using SAM [75), and missing background pixels were inpainted using LaMa (66 before composition.
C3DW 18], we utilized the orientation & illumination model checkpoint provided in the official implementation. This model is only capable of controlling azimuth angles for half-front views and was trained with scalar orientation values ranging from 0.0 to 0.5, where 0.0 corresponds to 90", and 0.5 corresponds to -90%, with intermediate orientations obtained through linear interpolation. Using this mapping, we converted ground truth (GT) orientations into the corresponding input values for the model.
About C3DW (18).
Handling Back-Facing Generation. We empirically observed that our base model struggled to generate back-facing images when the prompt did not explicitly contain view information. We hypothesized that this issue arises because high-reward samples lie within an extremely sparse region of the conditional probability space, making them inherently challenging to sample, even when employing our proposed approach. To address this issue, we explicitly added the phrase "facing back" in facing back cases (i.e., where 90 < df 270) and optimized the noise accordingly. With this simple adjustment, the model effectively generated both facing front and back images while maintaining high-quality outputs.
25

---

Table 5: Quantitative comparisons on General Orientation Controllability. ORIGEN maintains high accuracy even when evaluated on a more general set of samples. Best and second-best results are highlighted in bold and underlined, respectively.
Orientation Alignment
Text-to-Image Alignment
Id
Model
Azi, Ace. @22.5*
Azi, Abs.
Pol, Ace. 095" 1
Pol, Abs. Em..
Rot, Acc. @85"
Rot, Abs. Em.
CLIP
VQA
Pick
Frr. +
Score
0.432
43.89
0.511
13.07
0.969
0.259
0.234
2
MPGD 33
0.398
48.09
014855
12.25
0.967
1.79
0.258
0.707
0.234
FreeDoM 35
0.470
38.09
0.554
12.71
0.971
142
0.261
0.709
0,229
ReNO 22
0.586
41.41
0,502
14.37
0.958
2.69
0.253
0.676
0,216
ORIGEN (Ours)
0.777
24.96
0.575
12.46
0.969
1.52
0.263
0.710
0.219
Table 6: Quantitative comparisons 3D orientation grounded image generation for four primitive views. Best and second-best results are highlighted in bold and underlined, respectively.
3-View (tront, left right)
4.eeew (trent, left, right, back)
Model
Orientation Alignment
Test-to-Image Aligament
Orientation Alignment
Text-lo-mmae Aligument
Acc. 6/22.' 1
Abs. Er.
CLIP 1
VQA 1
PickScore
22.5%
Abs. Erz.
CLIP
VQA 1
PickScore
(1) One-step Texi-so Image Model
SD-Turbo
0.257
75.09
0.261
0.223
78.47
0.717
STXI--Tube
0.189
78.44
0.265
0.722
0.227
0.196
81.88
0.262
0.717
0.227
FLUX Schhelch
x
0.312
75.04
0,268
0.739
0.230
0:424
60.26
0,267
0,739
0,229
(2) Fine tuned Orientation Image Model
Zoro 1o.3
+
0.366
59.03
0.266
0.646
0,210 0.188
0,321
75.10
0.264
0.642
0.209
C3DW
64.77
0.187
(3) Cemerti-Gineittionn Methods with One Step Model
ORiGiN (Ours)
1.824
20.99
0.262
0.721
0.230
0.366
17.45
0.262
0.720
0.220
G Additional Results on the Extended ORIBENCH-SinglE Benchmark
We further evaluate ORIGEN under two additional scenarios that better reflect real-world use cases, by extending the ORIBENCH-Single Benchmark. The first experiment investigates the question: (1) "Can ORIGEN handle more general and complex orientations beyond simple angles?". The second experiment addresses: (2) "Can orientation grounding be achieved simply by prompting a text-to-image generative model?". For the first scenario, we evaluate ORIGEN and other guided generation methods
extended benchmark covering all three orientation components -erponnntt--aimuuhhh.. polar, and rotation without filtering the front range of azimuths. For the second scenario, we assess the capability of text-to-image models to perform orientation grounding for four primitive directions (front, left, right, back), where the orientation condition is provided via the input text prompt.
G.1 Orientation Grounding in more general and complex scenario.
We provide our extensive evaluation general curated ORIBENCH-Singe dataset. As discussed in Sec. [.1] we filter out non-clear object classes and image captions, but we do not apply filtering on the front range and do not fix the polar and rotation angles. Upon this, mix-match object classes and grounding orientations, forming a dataset consisting of 25 object classes, each with 40 samples, totaling IK samples. We evaluated on same metrics as in Sec. 4.2 in the main paper, including polar and rotation accuracy, within a tolerance of +5.0 as well as their absolute errors, following Wang er [20]. As shown in Tab. [5] ORIGEN generalizes well on diverse orientation conditions, outperforming all other training- free guidance methods. We also present qualitative comparisons for other training-fres guidance methods in Fig- G ORIGEN shows more consistent and accurate orientation alignment compared to other training-free approaches. This demonstrates the robustness of our approach, maintaining high orientation alignment performance even when evaluated on more general and complex set of samples.
G.2 Text-to-Image Models.
As baselines, we consider several one-step T2I generative models: SD-Turbo 24), SDXL-TTurbo 25 and FLUX-Schnell [21]. In particular, we appended a phrase that specifies the object orientation
26

---

Orientation
FooeDOM
ORIGEN (Ours)
dog on floor hiding under curtain.
bus drives on a paved road markings.
chair with some kind of stuffed animal on it."
"a chair next to a table that has electronic items on it.
Figure 6: Qualitative comparisons on generally extended ORIBENCH-Singl. benchmark.
Compared other vaining-free approaches [32 images with the given orientation grounding conditions.
33 35 22)
ORIGEN generates the best aligned
Orientation
SD-Turbo
24
SuXboTurbo
35
HLUX- Schnell P
Zeto- 1-to3 17
C3DW B
ORIGEN (Ours)
"Aop"
shopp fansh
"front"
person to rentounant holdnng plate of
"righe"
"o chair next a aable that her electronic frver on it
Figure 7: Qualitative comparisons on extend ORIBENCH-Singe benchmark for four primitive orientations.
27

---

User Study about Orientation Grounded Teat-to-lmage Generation
Pleas READ CAREFULLX Thank you for partoipation
Duration Time
ubcu1 nenotos
Iia dudy deviuu prolennau
aued hevrond sbueet ontentation deseripton.
You lager one senilad mage, You rape oost nauthe googt conditora pochholecc
Underttanding Orientation Condition
The anow indocato divcton in which "wee" or "hon of odsec
Presees
Flvier the tout co nitinn 10 eijee atteaation and grise drseptiin..
Cormiily seemowite caniiiin imagee
nigo motthes
that encerooni indudoo to shech wheche panoper ar on mhlill you dort the cornect evar the sgglnnce tasts, you son g aimmard.
The conaan sono oots
(a) Start page of the study.
7hta' out of 30 Considering the orientation and prompt conditions, Which train alta on the tracks by the platform.
follows ALL conditions below?
Choose A
Choose
Choose C
(b) Main page of the user study.
Figure 8: Screenshots of our user study.
the end of each caption in ORIBENCH- Single dataset. For more comprehensive comparisons, we also included the fine-tuned models (C3DW (18) and Zero- 1-t--3 [17]) considered in Sec. 4.5 in
this experiment well. As shown Tab. 6 ORIGeN significantly outperforms all baseline models in orientation alignment. Note that, although FLUX-Schneil [21] achieves the highest alignment among the vanilla T2I models, ORIGEN surpasses it by more than 2.5 times in the 3-view alignment setting (82.4% vs. 31.2%) and more than times in the 4-view alignment setting (86.6% vs. 42.4%) This substantial margin highlights the inherent ambiguity and lack of precise control in vanilla T2I approaches, orientation information embedded within textual descriptions is less explicit and reliable compared to direct orientation guidance. We further demonstrate the advantage of ORIGEN through qualitative comparisons in Fig. 7 Vanilla T2I models frequently fail to adhere strictly the desired orientation, even with directional phrases in text prompts. In contrast, ORIGEN consistently generates images accurately aligned with the specified orientations.
H User Study Examples
In this section, we provide details of the user study. To assess user preferences, we conducted an evaluation comparing images generated by Zero-1-to-3 [17) C3DW [18, and ORIGEN using ORIBENCH-Single as the benchmark. The study was conducted on Amazon Mechanical Turk (AMT). For each object class, sample was randomly selected, resulting in total of 25 questions.
28

---

Table Computational cost comparison of orientation grounding methods on single NVIDIA
A100 GPU.
Method
NFEs
Time per Iter (Total)
VRAM
Img Size
SD-Turbo 24
0.08s (0.08s)
4GB
512 x 512
SDXL-Turbo
25
I
0.30s (0.30s)
15GB
1024
1024
FLUX-Schnell
21
1.01s (1.01s)
32GB
512 512
C3DW [8
20
6.09s (0.30s)
6GB
768 768
Zero-1-to-3
50
8.19s (0.16s)
46GB
256 256
DPS 32
50
53.2s (1.06s)
43GB
512 512
MPGD [8
50
39.3s (0.79s)
36GB
512 512
FreeDoM 35
50
52.9s (1.06s)
43GB
512 x 512
ReNO 22
50
54.5s (1.09s)
43GB
512 512
ORIGEN (Ours)
50
52.7s (1.05s)
43GB
512
512
Table 8: Extended analysis on the reward-adaptive monitor function. We ablate (i) the slope parameter k and (ii) the step-size bounds (8min: Smax) to examine their effects on the sampling performance. For (i), (Smin: Smax) are fixed their default values of (} 3). and for (ii), le is fixed to its default value of 0.3. Results indicate that overall performance is robust to variations in these hyperparameters. Best and second- best values are highlighted in bold and underlined. respectively.
(1 Ablation on slope parameter k
(l) Ablation on step size bounds (6min Fmax)
Azi.
C1.IP 1
VQA 1
PickSoone
Smax
Azi. Acc.
CLIP
VQA
PickScore
0.7552
022
0.224
0.863
17.56
0.264
0.729
0.224
0.2
1.2
0.871
17.54
0.264
0.232
0.225
0.3
0.871
17.41
0.265
0.735
0.224
0.333
1,333
0.871
17.41
0.265
0.735
0.221
0.882
0.264
0.731
0.4
0.887
16:94
0.266
0.225
0.752
0.265
The orientations used to generate the images were intuitively visualized and presented alongside their corresponding prompts. As shown in Fig. participants were asked respond the following question: "Considering the orientation and prompt conditions, which image most folloos ALL the conditions below?". Each user study session included 25 test samples along with vigilance tests, which were randomly interspersed. The final results were derived from responses of valid participants who correctly answered at least three vigilance tests, leading total of 55 valid participants out of 100. No additional eligibility restrictions were imposed.
I Computational Costs
In Tab. we compare the computational costs of all baselines and ORIGEN in terms of inference time and GPU memory consumption. Our base model (FLUX-Schnell (1I) and all guided generation methods were evaluated for generating 512x512 images, while other methods followed their default settings. Guided generation methods generally slower due to the need for reward backpropagation. All methods were evaluated under setup of 50 Number of Function Evaluations (NFEs). Further analysis demonstrating the fast convergence of our wward-guided Langevin Dynamics is provided in Appendix C.2) suggesting that the computational cost can be partially reduced by using fewer NFEs. All measurements were conducted on single A100 GPU with 80GB of memory.
J Extended Analysis of the Reward-Adaptiv Monitor Function
To further understand the effect of our reward-adaptive monitor function introduced Sec. 3.4 we provide an extended analysis of its hyperparameters and their influence on model performance. As defined Eq. 99] the monitor function adaptively modulates the sampling step size based on reward, with three key hyperparameters: (i) & controlling the slope (how fast the step size decreases as the reward increases) and (ii) (8min; Smax) defining the lower and upper bounds of the adaptive step size range.
29

---

We systematically vary these hyperparameters to evaluate the sensitivity of our sampling dynamics and its robustness different configurations. Unless otherwise specified, all other hyperparameters are kept at their default values as reported in the main paper.
As shown in Tab. our performance remains robust across all hyperparameter settings, with only negligible variations in the evaluation metrics. The default values were selected through a coarse grid search rather than fine-tuning, indicating that ORIGEN does not rely fragile hyperparameter choices. Moreover, all variants with the reward-adaptive monitor further outperform the fixed-step baseline reported in Tab. of the main paper (denoted ORIGEN"), demonstrating its overall effectiveness.
K
More Comprehensive Comparisons with Training-Based Methods
To complement the thining-basede results presented in Tab. Tab. the main paper, we provide more com- prehensive quantitative comparisons with existing training-base approaches the ORIBENCH- Single benchmark. Detailed descriptions of the baselines are provided below, and the quantitative results are summarized in the subsequent subsection.
K.1 Baselines.
Zero-1-to-3. 17 Please refer to 'About Zero-1-to-3' section in Appendix
C3DW.
18 Please refer to 'About C3DW' section in Appendix
DDPO. 777 We evaluated RL-based fine-tuning method for orientation alignment. Specifically, we adopt DDPO (77), which fine-tunes a diffusion model via policy gradient updates on a learned reward signal. We note that existing RL-based diffusion fine-tuning methods are designed for either unconditional or text-conditional generation [48 49 77]. Thus, to enable orientation conditioning while leveraging existing code of DDPO, we opt to appending orientation phrases to the input text prompts (c.g.. "object is viewed from front-right, low-angle (azimuth 315%, polar 61%, rotation 90")"). oriennttnttioon paired training dataset using 50k orientations from the OrientAnything (20 training dataset and captions from the Cap3D dataset [78 79.. For other fine-tuning setups (e.g., hyperparameters)
To fine-tune with orientation grounding reward, constructed a single-object
primarily followed the default configuration of original DDPO to ensure fair comparisons.
SV3D. [8) SV3D is multi-view diffusion model generates images conditioned input image and target view angle. SV3D generates 21 views simultaneously, and we used the one generated image with target azimuth. The whole pipeline used in evaluation is same as Zero-I 1-to-3 [17], so please refer to About Zero-I-to-3' section in Appendix F
MV-Adapter. 81 MV MV-Adapter is multi-view diffusion model generates images conditioned on
input image and target view angle. MV-Adapter generates 6 views simultaneously, and we used the one generated image with target azimuth. The whole pipeline used in evaluation is same as Zero-1-to-3 177, so please refer to 'About Bero-1-to-2 section in Appendix
K.2 Results.
As shown in Table 9 ORIGEN achieves substantially higher orientation alignment accuracy than all training-bas based baselines while maintaining competitive text-to-image alignment performance. These results demonstrate that our inference-time reward-guie -uided sampling consistently outperforms training- based methods that rely explicit retraining or fine-tuning for orientation control.
L Reward-Guided Langevin Dynamics for Other Rewards
To assess the broader applicability of our reward-guided Langevin dynamics sampling, which is inherently reward-agnostic, we extended our experiments beyond 3D orientation grounding to additional tasks, including layout-to-image and depth-guided generation.
30

---

Table 9: Quantitative comparisons on Training- Methods. ORIGEN maintains high accuracy even when compared with more recent training-base' methods. Best and second-best results highlighted in bold and underlined, respectively.
Orientation Aligument
Text-to-Image Alignment
Id
Model
Azi, Acc. 6222.5"
Azi, Abs. Err.
CLIP
VQA
PickScore
Zero pom--1-tm
1-1o-3
17
0.499
59.03
0.272
0.663
0.213
0.426
64.77
DDPO FR
0.494
40.83
0.256
0.702
0.233
SV3D
80
0.410
60.226
0.274
0.313
0.196
MV Adupter
81
0.486
50.19
0.204
0.313
0.196
ORIGEN (Ours)
0.871
17.41
0.265
0.735
0.224
Table 10: Quantitative comparisons on layout-to-image and depth-guided generation. Best and second-best results are highlighted in bold and underlined, respectively.
Layout-to-lmage Generation
Depth-Guided Generation
1d Model
Spatial Alignment
Text-ln-Image Aligument
Dupth Alignment
Text-lo-Image Aligument
mloU 1
IRS
CLIP
VQA 1
PickScore
AbsRel 1.
CLIP
VQA 1
PickScore
FreeDeM
0.264
0.261
0.261
0.256
0.672
0.223
RaNo 22
0.287
70
0.261
0.655
0.229
6.67
0.265
0,705
0.238
ORIGEN (Ours)
0,344
72
0.284
0.659
0.229
4.62
0.764
0.686
0.330
L.1
Experimental Setup
We used FreeDoM strategy.
35 and ReNO
22 as baselines to highlight the effectiveness of our sampling
Layout-to-Image Generation. For evaluation setup, we randomly sampled 100 examples from the HRS-Spatial dataset (82) and used GroundingDINO [54] the reward model to guide object
positioning based on bounding-bow box conditions. We evaluated the results using mloU (measured by other detection model (3) and HRS score.
Depth-Guided Generation. For evaluation setup, we randomly sampled 100 image-caption pairs from the ORIBENCH-Single dataset and generated secudo-ground-oig truth depth maps us- ing DepthAnything-VV 84 We then used DepthAnything-V as reward model to guide image generation and evaluated the results using Absolute Relative Error (AbsRel).
L.2 Results
As shown in Fig. and Tab. TO ORIGEN consistently outperforms other quided-generation meth- ods [35 22 in condition alignment and image quality across both sayout-to-image and depth-guide generation tasks. These results demonstrate that our method generalizes effectively to diverse reward alignment tasks.
M More Qualitative Comparisons on Single Object Orientation Grounding
In the following, Fig. TO presents additional qualitative comparisons on the single object orientation grounding benchmarks.
N
More Qualitative Comparisons on Multi Object Orientation Grounding
We report more qualitative results on multi object orientation grounding (ORIBENCH-Mult) in Fig.
11
31

---

Condition
FreeDoM 35
ReNO 12
ORIGEN (Ours)
"a cat over airplane."
Layout Image
"a car helow horse and on the left of a cat."
"a zebra eating dry grass next fence."
Depth-Guided
"a dog looking away from the camera in the wind."
Figure 9: Qualitative comparisons on layout-to-image (rows 1-2) and depth-guided generation
(rows 3-4). ORIGEN achieves better condition alignment and overall image quality compared to other
quided-generation methods 35 22
32

---

Orientation
Zero-I-to-3
C3DW IR
Pro BS
ReNO
2
ORIGEN (Ours)
at zebra walking around looking for something to eat.
L
"a bench next to tamp post in the middle of a field."
"A close-sp of brown and white cow enclosure.
"a sitting desk with paper and pen."
Figure 10: Qualitative comparisons ORIBENCH-Sinen benchmark (Sec. 4.5). Compared to the existing orientation-tor -otiine-----meee models [8] ORIGEN generates the most realistic images, which also best align with the grounding conditions in the leftmost column.
33

---

FreeDoM
ORIGEN (Ours)
horse train.
"oore"
sheep and a tv
horse and a truck...
Figure 11: Qualitative comparisons ORIBENCH- benchmark (Sec. 4.5 Compared the guided-generation methods [32 33 35 22) ORIGEN generates the most realistic images, which also best align with the grounding conditions the leftmost column.
34


Reviews:
[{"review_id": "8p3e7YcmPF", "reviewer": "Reviewer_SEJH", "summary": "This paper introduces a zero-shot method for 3D orientation-conditioned text-to-image generation across multiple object categories. It relies on an off-the-shelf 3D orientation estimation model and a one-step text-to-image generative model. The technical contributions include:\n1. A sampling-based reward maximization approach using Langevin dynamics: inject random noise and perform gradient ascent.  \n2. An adaptive time rescaling method based on the reward function (smaller steps in high-reward regions and larger steps in low-reward regions).\n\nExperiments include:\n- A new benchmark comparing the proposed method with SOTA methods in terms of both orientation and text-to-image alignment.  \n- A user study comparing the proposed method with other SOTA text-to-image methods.", "questions": "See weaknesses.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n1. The zero-shot method for 3D orientation-conditioned text-to-image generation is novel and interesting.  \n2. The sampling-based reward maximization approach and adaptive time rescaling method are technically sound.  \n3. The proposed benchmark contributes to the community.  \n4. The presentation quality is good; the figures are clear and the writing is easy to follow.  \n5. The toy experiment presented in Fig. 2 is a nice addition.\n\n**Weaknesses:**\n\n1. There is no ablation study for the sampling-based reward maximization approach. It would be better to conduct an apple-to-apple comparison with a naive gradient ascent baseline.  \n2. A video visualization of continuous orientation change (e.g., the object rotating along an axis) would help demonstrate whether the object can truly follow the intended axis.", "was_revised": true, "final_justification": "The rebuttal addresses the issue raised by the reviewer. The final recommendation is Accept.", "rebuttal": "Thank you for your thoughtful and encouraging review. We sincerely appreciate your recognition of the novelty and technical soundness of our approach. We’re also grateful for your positive feedback on our reward-guided sampling, benchmark contributions, and the clarity of our presentation. Below, we provide detailed responses to your comments:\n\n**(1) Comparison with naïve gradient‑ascent method.**\n\nAs a kind reminder, we note that we already included comparisons with ReNO [22] (Tables 1 and 5), which is the naïve gradient ascent with noise regularization. The suggested comparison setting—the naïve gradient ascent method—can be interpreted as a simplified version of ReNO without noise regularization, and was not initially prioritized in our experiments, as we considered it an ablation on different types of regularization of ReNO.\n\nNevertheless, following your suggestion, we conducted an additional comparison with this naïve gradient ascent baseline. As shown in the table below, this method (denoted by Naïve GA) performs the worst in both orientation accuracy and text-to-image alignment. These results further highlight that the stochastic exploration inherent in our Langevin dynamics not only mitigates deviation from the original distribution but also improves alignment with the target reward signal. We thank your comment and will include these results in the revision.\n\n| Method | **Orientation Alignment** |  | **Text‑to‑Image Alignment** |  |  |\n|--------|---------------------------|--|-----------------------------|--|--|\n|        | Azi. Acc. @\\\\(22.5^{\\\\circ}\\\\) ↑ | Azi. Abs. Err. ↓ | CLIP ↑ | VQA ↑ | Pick Score ↑ |\n| Naïve GA            | 0.745 | 21.47 | 0.246 | 0.660 | 0.210 |\n| ReNO [22]           | *0.796* | *20.56* | *0.247* | *0.663* | *0.212* |\n| **ORIGEN (Ours)**   | **0.871** | **17.41** | **0.265** | **0.735** | **0.224** |\n\n*Best results are shown in **bold**, second‑best are *italics*. *\n\n**(2) Video visualization of continuous orientation change.**\n\nThank you for your helpful suggestion. We agree that adding a video visualization of continuous orientation change would be informative. Although we cannot directly show our video results due to the NeurIPS rebuttal policy that prohibits the inclusion of images or videos, we promise to add these video visualizations in the revision."}, {"review_id": "3UpvLqlaXZ", "reviewer": "Reviewer_6FJV", "summary": "This paper introduces ORIGEN, a zero-shot method for 3D orientation grounding in T2I generation. It proposes a reward-guided sampling method based on Langevin dynamics using a one-step generative model and a pretrained orientation estimator. It also designs a novel time-rescaling strategy to accelerate convergence. Experimental results on a new benchmark (ORIBENCH) show significant improvements over prior methods in both single- and multi-object scenarios.", "questions": "Please respond to the questions in the Weakness section.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1.First zero-shot method for 3D orientation grounding across multiple objects/diverse categories, addressing a critical gap in controllable generation.\n2.The proposed Langevin-based sampling approach is elegant, theoretically grounded, and simple to implement. It balances reward maximization and latent prior adherence.\n3.The paper is well-written, with clear problem formulation and method descriptions.\n4.The proposed reward function does not depend on a specific model architecture or dataset, which improves generality and future extensibility to other basic tasks (e.g., pose, layout).\n\nWeaknesses: \n1.The method is heavily reliant on Orient-Anything, a pretrained orientation estimator—its limitations directly affect ORIGEN’s performance and generalization.\n2.The method, while elegant, still requires ~50 iterations (as much as multi-step methods), making the “one-step model” narrative less compelling in practice.\n3.While the paper provides the values for hyperparameters used in reward-adaptive monitor function, it lacks an in-depth discussion on their sensitivity or the methodology for their selection, merely stating that these configurations provide a 'favorable balance'.\n4.In Section 3.1 (Why Test-Time Guidance?), the authors argue that reinforcement learning–based fine-tuning leads to image quality degradation. However, this assertion is not supported by any quantitative or qualitative evidence in the paper. No RL-based simplified baseline is included in the experimental comparisons.\n5.In Section 3.1 (Why Based on a One-Step Generative Model?), the authors attribute the poor performance of reward-guided optimization in diffusion models to intermediate-step issues (e.g., blurry early-stage outputs and minimal updates in late stages). However, the paper lacks concrete analysis (e.g., reward gradient magnitude over timesteps) or direct comparison with modern alternatives such as initial-noise optimization or terminal-sample guidance (e.g., InitNO, D-Flow). The dismissal of multi-step diffusion approaches appears oversimplified without sufficient experimental support.", "was_revised": true, "final_justification": "After carefully reading the reviews and the authors' response, I agree that the techniques in the submission are, to some extent, valuable for the community. However, the performance is heavily reliant on a single pretrained model for orientation estimation. From my perspective, this is significantly different from the LLM references claimed in the authors' rebuttal due to two main reasons. The first one is that the orientation estimation is a super-narrow research sub-area. This is different from general LLMs, where many foundational models are available. Therefore, relying on a single model makes it significantly vulnerable. Secondly, the pretrained model plays a vital role in this work, and the authors do not sufficiently show the generalizable ability of the proposed techniques.\n\nAfter comprehensively considering these aspects, I decided to raise my rating to Borderline Reject.", "rebuttal": "We sincerely appreciate your recognition of our work as the first zero-shot framework for 3D orientation grounding. Below, we respond to your concerns in detail:\n\n**(1) Reliance on the *OrientAnything* reward model.**\n\nAlthough the neural reward model (OrientAnything [20]) might not be perfect and could potentially affect our framework’s performance, our experimental results nevertheless demonstrate that our approach *significantly outperforms* existing orientation grounding methods (Table 1 in the paper).\n\nWe also note that, in related works on reward alignment for diffusion models [R1, R2, R3], most methods rely on neural reward models [R4, R5, R6] to enable alignment with objectives that are difficult to analytically define (e.g., text-to-image alignment, aesthetic preference). A similar trend is also widely observed in LLM reasoning, where neural reward models are used to evaluate and guide multi-step reasoning processes [R7, R8]. We similarly follow this paradigm to use a pretrained neural model to define a reward function tailored to our task.\n\n**(2) Sampling ~50 steps, making the one-step model narrative less compelling.**\n\nAs discussed in Sec. 3.1 (Why Based on a One-Step Generative Model?), our main purpose for using a one-step generative model is to enable clearer and more reliable reward estimation, rather than to speed up image generation. Note that our reward model (OrientAnything [20]) was trained on, and expects, a clear image as input to compute accurate rewards. However, images generated by multi-step generative models in early steps are often blurry due to the high variance in their marginal distribution. For further discussion on this particular point, we kindly refer the reader to (5) below.\n\nAlthough efficiency was not our primary motivation for adopting a one-step generative model, we also provided a computation time analysis in Sec. 4.7 in the paper. There, we discussed that our method’s number of function evaluations (NFEs) was set to 50 to perform fair comparisons with the multi-step baselines to match the number of their denoising steps. In practice, our method requires only an average of 12.8 NFEs—*approximately 4× fewer than the 50 NFEs used in our reported experiments*—to achieve superior orientation alignment than all the baseline methods. Please refer to Sec. 4.7 for the full analysis.\n\n**(3) In-depth discussion about Reward-adaptive Monitor Function.**\n\nIn our proposed monitor function, \\\\(\\\\mathcal{G}(x) = s_{\\\\min} - \\\\tanh (k x) \\\\cdot (s_{\\\\max} - s_{\\\\min})\\\\), hyper-parameters include \\\\(k \\\\) controlling the *slope* (how the step size shrinks as the reward rises), and \\\\(s_{\\\\min}, s_{\\\\max} \\\\) bounding the step size values. Below, we present additional experimental results to assess the sensitivity of these hyper-parameters; note that hyper-parameters are set to their default values in the main paper unless otherwise specified.\n\n#### (i) Ablation on \\\\(k \\\\)\n\n| \\\\(k\\\\) | **Orientation Alignment** |  | **Text‑to‑Image Alignment** |  |  |\n|--------|---------------------------|--|-----------------------------|--|--|\n|     |  Azi. Acc. @\\\\(22.5^{\\\\circ}\\\\) ↑ | Azi. Abs. Err. ↓ | CLIP ↑ | VQA ↑ | Pick Score ↑ |\n|   0.1 | 0.861 | 17.93 | **0.267** | *0.732* | **0.226** |\n|   0.2 | 0.863 | 17.56 | 0.264 | 0.729 | 0.224 |\n|   0.3 | 0.871 | *17.41* | *0.265* | **0.735** | 0.224 |\n|  0.4 | **0.882** | **17.17** | 0.264 | 0.731 | *0.225* |\n|  0.5 | *0.876* | 17.54 | 0.264 | *0.732* | 0.224 |\n\n#### (ii) Ablation on \\\\(s_{\\\\min}\\\\), \\\\(s_{\\\\max}\\\\)\n\n| \\\\(s_{\\\\min}\\\\) | \\\\(s_{\\\\max}\\\\) | **Orientation Alignment** |  | **Text‑to‑Image Alignment** |  |  |\n|--|--|-------------------------|--|-----------------------------------|-|-|\n|     |     |  Azi. Acc. @\\\\(22.5^{\\\\circ}\\\\) ↑ | Azi. Abs. Err. ↓ | CLIP ↑ | VQA ↑ | Pick Score ↑ |\n| 0.2 | 1.6 | 0.881 | **16.72** | 0.264 | 0.729 | *0.224* |\n| 0.2 | 1.2 | 0.871 | 17.64 | 0.264 | 0.732 | **0.225** |\n| 0.333 | 1.333 | 0.871 | 17.41 | *0.265* | **0.735** | *0.224* |\n| 0.2 | 1.2 | **0.887** | *16.94* | **0.266** | *0.734* | **0.225** |\n| 0.6 | 1.6 | *0.884* | 17.14 | *0.265* | 0.733 | *0.224* |\n\n*Best results are shown in **bold**, second‑best are *italics*. *\n\n**Take-away.** Our performance is *robust* to all three hyper-parameters: changing \\\\(k, s_{\\\\min}, s_{\\\\max} \\\\) has negligible impact on all evaluation metrics. Our defaults were chosen by a coarse grid search, not to finely tune peak scores, confirming that ORIGEN does not rely on fragile hyper-parameter selection. Moreover, all variants with the reward-adaptive monitor consistently outperform the setting without it (Table 1 in main paper), highlighting its overall effectiveness.\n\n\n**(4) Comparison with RL-based fine-tuning baseline.**\n\nFollowing your suggestion, we provide additional comparison results with a well-known RL-based fine-tuning method, DDPO [50], which fine-tunes a diffusion model via policy gradient updates on a learned reward signal. We note that existing RL-based diffusion fine-tuning methods are designed for either unconditional or text-conditional generation [48, 50, 51]. Thus, to enable orientation conditioning while leveraging existing code of DDPO, we opt to appending orientation phrases to the input text prompts (e.g., “~. {object} is viewed from front-right, low-angle (azimuth 315°, polar 61°, rotation 90°)”).\nTo fine-tune with our orientation grounding reward, we constructed a single-object text–orientation paired training dataset using 50k orientations from the OrientAnything training set and captions from the Cap3D dataset [R9, R10]. For other fine-tuning setups (e.g., hyper-parameters), we primarily followed the original DDPO configuration to ensure fair comparisons, and we will release our full implementation.\n\nAs shown in the table below, DDPO-based fine-tuning demonstrates limited improvement of the orientation alignment over the base model (FLUX.1-dev). Moreover, it also results in substantial degradation in image quality, as we described in L106–109. In contrast, our method achieves the highest performance in both orientation and text-to-image alignment, while preserving image fidelity.\n\n| Method | **Orientation Alignment** |  | **Text‑to‑Image Alignment** |  |  |\n|--------|---------------------------|--|-----------------------------|--|--|\n|        | Azi. Acc. @\\\\(22.5^{\\\\circ}\\\\) ↑ | Azi. Abs. Err. ↓ | CLIP ↑ | VQA ↑ | Pick Score ↑ |\n| Base Model (FLUX.1-dev) [21] |  0.395 | 52.14 | **0.268** | **0.739** | **0.234** |\n| DDPO [50] | *0.494* | *40.83* | 0.256 | 0.702 | 0.223 |\n| **ORIGEN (Ours)**   | **0.871** | **17.41** | *0.265* | *0.735* | *0.224* |\n\n\n**(5) Lack of analysis about multi-step guided generation methods**\n\nPlease note that we already included comparisons with multi-step diffusion-based methods, which are DPS, MPGD, FreeDoM in Tables 1 and 5 of the paper, where we experimentally validated their suboptimal performance. We respectfully clarify that the phenomenon of \"blurry early-stage outputs and minimal updates in late stages\" is widely discussed in the existing literature [R11, R12, R13, R14]. For visual evidence, please refer to Fig. 5 and Fig. 6 in DDPM [R15], and Fig. 1 in EDM [R16], which illustrate the blurry Tweedie estimations in the early timesteps.\n\nFor the additional baselines suggested in the review (InitNO, D-Flow), both approaches are either infeasible (InitNO) or lack accessible implementations (D-Flow):\n\n**InitNO [45]** was originally designed for an attention score reward, which can be computed after very few reverse steps (typically 1-step). However, the orientation grounding reward in our task must be evaluated on the clean image, requiring backpropagation through much larger timesteps—making it infeasible given current GPU memory limitations.\n\n**D-Flow [43]** addresses the memory issue via heavy gradient checkpointing, but no public code is available. We contacted the authors to request the implementation but did not receive a response. Aside from this code accessibility issue, we note that D-Flow reports a computation time of *3–40 minutes* per prompt, making it orders of magnitude slower than our method.\n\n\n---\n\n**References**\n\n**[R1]** Diffusion Model Alignment Using Direct Preference Optimization, Wallace *et al.,* CVPR 2024\\\n**[R2]** Test-time Alignment of Diffusion Models without Reward Over-optimization, Kim *et al.,* ICLR 2025\\\n**[R3]** Training Diffusion Models with Reinforcement Learning, Black *et al.,* ICLR 2024\\\n**[R4]** Laion Aesthetic Predictor, Schuhmann *et al.,* 2022\\\n**[R5]** ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation, Xu *et al.,* NeurIPS 2023\\\n**[R6]** Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation, Kirstain *et al.,* NeurIPS 2023\\\n**[R7]** Training verifiers to solve math word problems, Cobbe *et al.,* arXiv 2110.14168\\\n**[R8]** Let's Verify Step by Step, Lightman *et al.,* ICLR 2024\\\n**[R9]** Objaverse: A Universe of Annotated 3D Objects, Deitke *et al.,* CVPR 2023\\\n**[R10]** Scalable 3D Captioning with Pretrained Models, Luo *et al.,* NeurIPS 2023\\\n**[R11]** eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers, Balaji *et al.,* arXiv 2211.01324\\\n**[R12]** Prompt-to-Prompt Image Editing with Cross Attention Control, Hertz *et al.,* ICLR 2023\\\n**[R13]** Perception Prioritized Training of Diffusion Models, Choi *et al.,* CVPR 2022\\\n**[R14]** R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation, Xiao *et al.,* ICLR 2024\\\n**[R15]** Denoising Diffusion Probabilistic Models, Ho *et al.,* NeurIPS 2020\\\n**[R16]** Elucidating the Design Space of Diffusion-Based Generative Models, Karras *et al.,* NeurIPS 2022"}, {"review_id": "UpXCvMhvZv", "reviewer": "Reviewer_D7en", "summary": "The paper introduces ​ORIGEN, a zero-shot method for 3D orientation grounding in text-to-image (T2I) generation. Unlike prior work focused on 2D spatial control (e.g., bounding boxes), ORIGEN enables precise 3D orientation control for multiple objects across diverse categories without requiring task-specific training data. The key innovation is a ​reward-guided sampling approach​ leveraging a pretrained discriminative model (OrientAnything) for orientation estimation and a one-step generative flow model (FLUX-Schnell). To address challenges like local optima and realism degradation in gradient-based optimization, ORIGEN proposes ​Langevin dynamics-based sampling​ with noise injection and ​reward-adaptive time rescaling​ for faster convergence. Experiments on the curated ORIBENCH benchmark (MS-COCO-based) show ORIGEN outperforms training-based (e.g., Zero-1-to-3, C3DW) and test-time guidance baselines (e.g., DPS, ReNO) in orientation alignment while maintaining text-image fidelity. User studies further validate its superiority (58.18% preference).", "questions": "1. ​Failure Cases: What are the most common failure modes (e.g., misalignment due to OrientAnything’s errors)? Would combining text prompts with explicit orientation cues improve robustness?\n2. ​Reward Function Flexibility: Can the sampling method be adapted to different reward functions for broader controllability? If yes, how does it compare to other guidance-based approaches?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1. ​Novelty: First zero-shot method for multi-object 3D orientation grounding in T2I generation, addressing a gap in prior work limited to single-object/synthetic data.\n2. ​Strong Empirical Results: Outperforms baselines on ORIBENCH (85.4% Acc.@22.5° for single-object, 69.2% for multi-object) and user studies, demonstrating generalization to real-world images.\n\n\nWeaknesses​\n\n1. ​Limited 3D Control: While object orientation is represented by three Euler angles, the results primarily demonstrate control over the xy-plane. The lack of variation in the upright (z-axis) direction suggests the method may not fully exploit 3D orientation grounding.\n​2. Missing Ablation Studies: Given that ORIGEN is training-free, conducting ablation studies (e.g., validating the impact of ​Reward-Guided Langevin Dynamics​ and ​Reward-Adaptive Time Rescaling) would strengthen the claims. Such experiments should be computationally feasible and could provide deeper insights into the method’s components.\n3. ​Generalizability to Other Controls: The proposed sampling method is agnostic to the reward function. Could it be extended to other spatial attributes (e.g., pose, depth) by swapping the reward function? If so, a comparison with training-free guided methods like ​FreeDoM​ would highlight its versatility.", "was_revised": true, "final_justification": "The authors have provided additional quantitative results on diverse 3D control, spatial grounding, and depth-conditioned generation, which address some of my concerns. Given these improvements, this method could serve as a promising training-free alternative for guided image generation. Accordingly, I have decided to raise my score to ​Borderline Accept—provided the authors include these additional experiments in the final version.", "rebuttal": "Thank you for your thoughtful and encouraging review. We appreciate your recognition of our method’s novelty and strong empirical results. Below, we provide detailed responses to your comments:\n\n**(1) Limited 3D control.**\n\nWhile our model is fully capable of controlling the complete 3D orientation—including variations along the z-axis—we designed our experimental setup within the xy-plane to enable comparisons with the baseline methods, which do not support full 3D controllability (L244-246). While we are unable to show additional image results here due to the NeurIPS policy prohibiting the inclusion of images in the rebuttal, we promise to add more such examples in the revised version to better showcase the versatility of our approach beyond the xy-plane.\n\n**(2) Ablation study on Langevin Dynamics.**\n\nPlease note that we have already included experiments validating both our Reward-Guided Langevin Dynamics and Reward-Adaptive Time Rescaling in Table 1 in the paper. To evaluate the effectiveness of Reward-Guided Langevin Dynamics, we included ReNO [22], which uses gradient ascent with noise regularization, as a baseline in the table. \n\nFor a more comprehensive comparison, here, we show additional comparisons with a naïve gradient ascent method (i.e., ReNO without noise regularization) in the table below. Our method achieves superior results compared to all these baselines, demonstrating the advantages of Reward-Guided Langevin Dynamics in both orientation alignment and text-to-image alignment.\n\nTo validate the effectiveness of Reward-Adaptive Time Rescaling, we already provided an ablation in Table 1 in the main paper, comparing ORIGEN* (without time rescaling) and ORIGEN (with time rescaling), where our final method with time rescaling achieves clearly improved performance.\n\n\n| Method           | **Orientation Alignment** |   | **Text‑to‑Image Alignment** |    |    |\n|------------------|----------------------------------|-------------------------|------------|-|---|\n|                  | Azi. Acc. @ 22.5 ↑ | Azi. Abs. Err. ↓        | CLIP ↑                      | VQA ↑  | PickScore ↑  |\n| Naïve GA         | 0.745    | 21.47     | 0.246          | 0.660 | 0.210   |\n| ReNO [22]        | *0.796*   | *20.56*         | *0.247*   | *0.663* | *0.212* |\n| **ORIGEN (Ours)**| **0.871**            | **17.41**  | **0.265**    | **0.735** | **0.224** |\n\n\n*Best results are shown in **bold**, second‑best are *italics*. *\n\n\n**(3) Other reward function experiments.**\n\nThank you for your thoughtful suggestion regarding the potential extensions of our work. As you kindly pointed out, our method is reward-agnostic and can be potentially extended to other reward alignment tasks such as spatial grounding, depth, or pose. To demonstrate its generality, we conducted additional experiments on **spatial grounding** (i.e., bounding-box-conditioned 2D position control) and **depth-conditioned image generation**. In both setups, we used FreeDoM [35] and ReNO [22] as baselines to highlight the effectiveness of our sampling strategy.\n\n---\n\n**Spatial Grounding.** For this experiment, we randomly sampled 100 examples from the HRS-Bench dataset [R1]. We used GroundingDINO [55] as the reward model to guide object positioning based on bounding-box conditions. As evaluation metrics, we report spatial accuracy using mIoU (measured by SalienceDETR [R2]) and HRS score, and text-image alignment using CLIP similarity, VQA score, and PickScore.\n\n| Method            | **Spatial Alignment** |              | **Text-to-Image Alignment** |        |               |\n|------------------|------------------------|--------------|-----------------------------|--------|---------------|\n|                  | mIoU ↑      | HRS ↑        | CLIP ↑         | VQA ↑  | PickScore ↑   |\n| FreeDoM [35]      | 0.244                  | 60        | 0.261                       | 0.640  | *0.221*          |\n| ReNO [22]         | *0.287*                  | *70*        | *0.283*                     | *0.655*  | **0.229**        |\n| **ORIGEN (Ours)**| **0.344**              | **72**    | **0.284**                   | **0.659** | **0.229**  |\n\n---\n\n**Depth-Conditioned Generation.** For this setup, we randomly sampled 100 image-caption pairs from the ORIBENCH-Single dataset and generated pseudo-ground-truth depth maps using DepthAnything-V2 [R3]. We then used DepthAnything-V2 as a reward model to guide image generation and evaluated the results using Absolute Relative Error (AbsRel).\n\n| Method            | **Depth Alignment**  | **Text-to-Image Alignment** |        |               |\n|------------------|----------------------|-----------------------------|--------|---------------|\n|                  | AbsRel ↓            | CLIP ↑        | VQA ↑                       | PickScore ↑ |\n| FreeDoM [35]      | *6.34*               | 0.256         | 0.672                       | 0.223        |\n| ReNO [22]         | 6.67              | **0.265**       | **0.705**                       | **0.233**      |\n| **ORIGEN (Ours)**| **4.62**           | *0.264*     | *0.686*                   | *0.230*    |\n\nThese results demonstrate that our method is reward-agnostic and effectively generalizes to different types of guidance signals, such as 2D spatial constraints and depth cues, without requiring task-specific model modifications. While this was originally raised as a limitation of our method, *we believe it instead highlights our method’s strength—its generalizability and potential applicability to broader tasks in the future*. \n\n\n**(4) Discussion on failure cases and text prompts with explicit orientation cues.**\n\n**Failure cases.** Generating images of objects with inherently ambiguous orientations (e.g., objects with little distinction between front and back) can indeed be challenging to handle in our framework. We will make sure to clarify these limitations in the revised version of the paper.\n\n**Text prompts with explicit orientation cues.** We conducted additional ablation experiments using text prompts augmented with explicit orientation cues. We considered two settings: (1) adding text prefixes with abstracted orientations (e.g., “{object} is viewed from front-right”), and (2) adding text prefixes with both abstracted and numerical orientations (e.g., “{object} is viewed from front-right, low-angle (azimuth 315°, polar 61°, rotation 90°)”). As shown in the table below, these settings result in negligible effects in orientation grounding performance on the ORIBENCH-Single benchmark, suggesting that orientation grounding cannot be trivially improved by simply adding explicit orientation phrases to the prompt—reinforcing the necessity of our reward-guided sampling approach. We will include this discussion in the revised version of the paper.\n\n| Method             | **Azi. Acc. @ 22.5° ↑** | **Azi. Abs. Err. ↓** |\n|--------------------|--------------------------|------------------------|\n| Abstract        | *0.867*                   | *17.96*                 |\n| Abstract + Numerical           | 0.856                    | 18.51                 |\n| **ORIGEN (Ours)**  | **0.871**                | **17.41**             |\n\n---\n\n**References**\n\n**[R1]** HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models, Bakr *et al.,* ICCV 2023\\\n**[R2]** Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement, Hou *et al.,* CVPR 2024\\\n**[R3]** Depth Anything V2, Yang *et al.,* NeurIPS 2024"}, {"review_id": "5kn4jJRGVE", "reviewer": "Reviewer_rW2t", "summary": "ORIGEN introduces the first zero-shot framework that grounds 3-D orientations for multiple objects during text-to-image generation. It combines a one-step latent flow generator with a reward from a pretrained orientation estimator, then performs Langevin-style sampling (gradient + noise) plus reward-adaptive step sizing to maintain realism while enforcing pose. Experiments on a new MS-COCO-based benchmark and user studies show clear gains over training-based baselines (Zero-1-to-3, Continuous3DWords) as well as prompt-only or test-time guidance methods.", "questions": "I generally agree with the overall presentation and motivation and therefore lean toward acceptance; however, due to some practical concerns, I currently rate it as borderline accept. I hope the authors will consider the following in their rebuttal:\n\n- Provide more comparative baselines methods.\n- Analyze whether the proposed method can be applied to image editing, enabling control over object orientation within images.\n- Include additional generated examples to further demonstrate the method’s effectiveness.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n- The visual results presented are highly impressive.\n- The paper’s logic is coherent; the toy experiment effectively demonstrates the method’s validity, particularly highlighting the advantage of reward-adaptive time rescaling.\n- The experimental design is solid, and the method exhibits clear benefits.\n\nWeaknesses\n\n- The paper’s objective resembles motion control approaches, a direction that has been explored extensively; from my perspective, the results shown here lack some novelty.\n- The practical application scope of text-to-image orientation control is rather limited, and I have concerns about the stability of a zero-shot method.\n- The most meaningful application seems to be image editing tasks similar to Zero-123 for controlling object orientation, but Zero-123 is already quite old and many multi-view generation methods now exist. The authors should include additional experiments.", "was_revised": true, "final_justification": "The authors have solved most of my questions. Therefore, I have decided to maintain my score as borderline accept.", "rebuttal": "Thank you for your positive and constructive feedback. We appreciate your recognition of the clarity and coherence of our method, the effectiveness of our reward-adaptive sampling strategy, and the strength of our visual and experimental results. We address your comments in detail below:\n\n**(1) Difference with motion-control approaches.**\n\nAs “motion control approaches” may refer to a broad range of domains (e.g., camera trajectory control, object animation), we would appreciate it if the reviewer could kindly clarify which specific line of work is being referenced. We would be glad to address the comparison more directly.\nIn general, our method focuses on grounding the static 3D object orientations during image generation from scratch, rather than manipulating their motions over time. As such, we believe our problem formulation and methodology are distinct from works that address temporal motion control.\n\n**(2) Scope and stability of zero-shot approaches.**\n\n**Scope.** Note that spatial grounding in image generation is one of the most active research fields [1, 2, 3, R1, R2]. While the existing spatial grounding methods were limited to 2D position grounding, ORIGEN makes a significant contribution by extending the grounding modality to **3D orientation**, which has only been explored in limited forms in prior works.\n\n**Zero-shot approaches.** We respectfully disagree with the claim that zero-shot methods are generally less stable than training-based methods. As discussed in the paper, training or fine-tuning a generative model conditioned on object orientation is non-trivial due to the lack of a large-scale dataset containing (prompt, image, orientation) samples. Consequently, existing fine-tuning-based approaches such as [18] exhibit clear limitations in both orientation controllability and realism, as evidenced by our comparisons in Tables 1 and 6 in the paper. In contrast, our method does not require training a base model and can fully leverage the rich prior knowledge embedded in large pre-trained text-to-image models. This allows for the generation of high-quality images across diverse prompts, object categories, and orientations, making our approach both practical and effective.\n\n**(3) More comparative baseline methods.**\n\nFollowing your suggestion, we performed additional baseline comparisons with state-of-the-art multi-view diffusion models more recent than Zero-1-to-3 — SV3D (ECCV 2024) [R3] and MV-Adapter (ICCV 2025) [R4] — on the ORIBENCH-Single benchmark. As shown in the table below, our method outperforms all these additional baselines, demonstrating the effectiveness of the proposed Reward-Guided Langevin Dynamics and Reward-Adaptive Time Rescaling. We thank your suggestion and will include these additional results in the revision.\n\n---\n**Table.** Comparison on ORIBENCH-Single. SV3D and MV-Adapter generate 6 and 21 views respectively; the one generated image with target azimuth is selected for evaluation. All models use identical prompts and target azimuths. Best results are shown in **bold**, second‑best are *italics*.\n| Method | **Orientation Alignment** |  | **Text‑to‑Image Alignment** |  |  |\n|--------|---------------------------|--|-----------------------------|--|--|\n|        | Azi. Acc. @\\\\(22.5^{\\\\circ}\\\\) ↑ | Azi. Abs. Err. ↓ | CLIP ↑ | VQA ↑ | Pick Score ↑ |\n| Zero-1-to-3           | *0.499* | 59.03 | 0.272 | 0.663 | 0.213 |\n| C3DW           | 0.426 | 64.77 | 0.220 | 0.439 | 0.197 |\n| SV3D [R3] | 0.410 | 60.26 | *0.274* | *0.678* | *0.214* |\n| MV-Adapter [R4] | 0.486 | *50.19* | 0.204 | 0.313 | 0.196 |\n| **ORIGEN (Ours)**   | **0.871** | **17.41** | **0.265** | **0.735** | **0.224** |\n\n\n**(4) Targeted problem scope (vs. image editing).**\n\nWhile we agree that image editing with object orientation control is a potentially useful application, we did not initially prioritize this task, as spatial grounding itself is considered a stand-alone application in the literature, and the related prior works [1, 2, 3, R1, R2] also do not necessarily address image editing. We believe the nature of these two tasks differs: grounded image generation focuses on generating images from scratch while aligning with grounding conditions, whereas image editing aims to modify an existing image while preserving its original content.\n\nNevertheless, we agree that image editing with orientation control is another valuable direction. Our method could potentially be extended to this task by integrating personalized generation techniques or incorporating reward terms that encourage content consistency with a reference image [R5, R6]. We promise to include these additional image editing application results in the revision.\n\n\n**(5) Additional generated examples.**\n\nAlthough we cannot include additional examples due to the NeurIPS policy prohibiting the inclusion of images or videos in the rebuttal, we promise to include additional qualitative results—covering more diverse object categories, captions, and orientations—in the revision. We thank your valuable suggestion.\n\n---\n\n**References**\n\n**[R1]** BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion, Xie *et al.,* ICCV 2023\\\n**[R2]** R&B: Region and Boundary Aware Zero-shot Grounded Text-to-Image Generation, Xiao *et al.,* ICLR 2024\\\n**[R3]** SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion, Voleti *et al.,* ECCV 2024\\\n**[R4]** MV-Adapter: Multi-view Consistent Image Generation Made Easy, Huang *et al.,* ICCV 2025\\\n**[R5]** Improving Editability in Image Generation with Layer-wise Memory, Kim *et al.,* CVPR 2025 \\\n**[R6]** DreamBooth: Fine-Tuning Text-to-Image Diffusion Models for Subject-Driven Generation, Ruiz *et al.,* CVPR 2023"}]

Author rebuttal:

Meta-review: The paper presents a method for text-to-image generation with control over the 3D orientation of objects in the generated image. The method leverages an off-the-shelf 3D orientation estimation model, a pre-trained text-to-image model, and achieves test-time orientation control using a guided-sampling approach based on Langevin dynamics, where a sampled latent is chosen to maximize a reward function based on the target orientation. 

Reviewers appreciated the technical contributions related to sampling the image latent and the approach to adaptive time rescaling, the general applicability of the method to different models and datasets, the strong results, and the problem setting (zero-shot multi-object orientation control in text-to-image generation). However, they also raised concerns about whether the method can handle variations along the z-axis, the lack of comparisons to more recent baseline methods, the reliance on the pre-trained orientation estimation model, and the requirement for ~50 iterations for image generation. 

The authors responded to these concerns in their rebuttal, and three reviewers entered a positive rating. These reviewers noted that their concerns were addressed, or that they would be satisfied with acceptance given the rebuttal results can be included in the paper (which the authors have promised). Although one reviewer remained negative towards the paper, they also noted that the techniques would be valuable for the community. After reading the paper and considering the concerns, I am recommending the paper be accepted, and I encourage the authors to incorporate the results from the rebuttal in the final version.
Decision: Accept (poster)