MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training-Free Rectification
Authors: Tianchi Xing
Bonan Li
Congying Han
Xinmin Qiu
Zicheng Zhang
Tiande Guo
Venue: ICML 2025 poster (2025)
Keywords: 3D Generation
Diffusion Model
Training-Free
Primary area: applications->computer_vision
TL;DR: We introduce a plug-and-play method that rectifies multi-view inconsistencies in images generated by multi-view diffusion models, functioning in a training-free manner.

Abstract: Multi-view Diffusion has greatly advanced the development of 3D content creation by generating multiple images from distinct views, achieving remarkable photorealistic results. However, existing works are still vulnerable to inconsistent 3D geometric structures (commonly known as Janus Problem) and severe artifacts. In this paper, we introduce MIRROR, a versatile plug-and-play method that rectifies such inconsistencies in a training-free manner, enabling the acquisition of high-fidelity, realistic structures without compromising diversity. Our key idea focuses on tracing the motion trajectory of physical points across adjacent viewpoints, enabling rectifications based on neighboring observations of the same region. Technically, MIRROR comprises two core modules: Trajectory Tracking Module (TTM) for pixel-wise trajectory tracking that labels identical points across views, and Feature Rectification Module (FRM) for explicitly adjustment of each pixel embedding on noisy synthesized images by minimizing the distance to corresponding block features in neighboring views, thereby achieving consistent outputs. Extensive evaluations demonstrate that MIRROR can seamlessly integrate with a diverse range of off-the-shelf object-level multi-view diffusion models, significantly enhancing both the consistency and the fidelity in an efficient way.

MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training-Free Rectification
Tianchi Xing
Bonan Li
Congying Han
Xinmin Qiu
Zicheng Zhang'
Tiande Guo
+MIROO Syncrreames
More consistent
+MIRROR VideoMV
beauttfull Sastriaate busterfiy. an Pasee
A pig weening 0 tackpack sp Dsseet.
More consistent
A oute atsarppunnke sephontt 3b ossee.
Figure 1: Generated multi-viee images by applying MIRROR on SyncDreamer (Liu et al., 2023b) and VidcoMV (Zuo et al., 2024) for rectification. MIRROR effectively improves the consistency of generated results in both image-based and base- multi-view generation models.
Abstract
Technically, MIRROR comprises two core mod- ules: Trajectory Tracking Module (TTM) for pixel-wise trajectory tracking that labels identical points across views, and Feature Rectification Module (FRM) for explicitly adjustment of each pixel embedding on noisy synthesized images by minimizing the distance to corresponding block features in neighboring views, thereby achieving consistent outputs. Extensive evaluations demon- strate that MIRROR, through seamless integra- tion with variety of off-the-shelf object-level multi-view diffusion models, achieves efficient improvements in consistency and fidelity, thereby functioning as a practical rectification tool.
Multi-view Diffusion has greatly advanced the de- velopment of 3D content creation by generating multiple images from distinet views, achieving remarkable photorealistic results. However, ex- isting works still vulnerable inconsistent 3D geometric structures (commonly known as Janus Problem) and severe artifacts. In this paper, we introduce MIRROR, a versatile plug-and-play method that rectifies such inconsistencies a training-fres manner, enabling the acquisition of high-fidelity, realistic structures without compro- mising diversity. Our key idea focuses tracing the motion trajectory of physical points across adjacent viewpoints, enabling rectifications based on neighboring observations of the same region.
1. Introduction
Eqqul contribution 'Department of Mathematical Sciences, University of Chinese Academy of Sciences. No. 19A Yuquan Road, Shijingshan District, Beijing, China. Correspondence to: Congying Han <hancy @ucas.ac.cn>.
Recently, 3D asset generation has become of the most promising fields in computer vision, and demonstrated ex- tensive application in artworks and creation. Inspired by the success text-to-image generation (Rombach et al., 2022), text-to-3D generation models have increasingly attracted significant attention. Regarding the scarcity of 3D data, the
Proceedings of the nd International Conference Machine Learning, Vancouver, Canada, PMLR 267, 2025. Copyright 2025 by the author(s).

---

MIRROR: Make Your Object-Levvl Multi-View Generation More Consistent with Training-Fre Rectification
seminal works leverages 2D diffusion models as priors to optimize 3D representations using score distillation (Poole et al., 2022; Liu et al., 2023a; Wang et al., 2023; Lin et al.,
Input
MIRROR
Output
Protrained
Vulti-Viww Diffusion
2023; Qian et al., 2023; Wang et al., 2024b). Unfortunately, these methods suffer from serious inconsistencies, most no-
X Inconsldernt
Comnectent
tably the Janus Problem and content drifting, due to the lack of 3D prior knowledge, To address these issues, MV- Dream (Shi et al., 2023b) was the first to introduce the multi-view diffusion model, leveraging joint training on 2D and 3D data to simultaneously generate four orthogonal views. Building upon it, multi-view diffusion models such as SyncDreamer (Liu et al., 2023b), MVD-Fusion (Hu et al., 2024) and VideoMV (Zuo et al., 2024) have been proposed to employ strategies for spatial and temporal alignment to enhance multi-view consistency, thereby enabling the simul- tancous generation of dense views the object level.
Figure 2: High-level Overview of MIRROR. This is a plug- and-play rectification technique that can be seamlessly in- corporated into multi-view diffusion models to enhance consistency and fidelity without any training or fine-tuning.
explicit geometric constraints and rely solely on implicit learning through network weights. Given the limited avail- ability of 3D data, such methods are prone to becoming trapped in local optima and often exhibit poor generaliza- tion. Although CoSER (Li et al., 2024a) attempts to address this by injecting local spatial correspondence information from neighboring views into the cross-view attention to promote multi-view consistency, its effectiveness remains limited due the absence of depth information-which results in inaccurate view correspondences and the intrin- sic challenges in optimizing attention layers as discussed carlier. Therefore, unlike previous approaches that rely on fine-tuning within the attention mechanism, we propose Fea- ture Rectification Module (FRM), which, without requiring any training or fine-tuning, performs feature rectification directly on the predicted images during inference, thereby achieving enhanced performance in more streamlined and efficient manner. To this end, we compute the similarity between each pixel's feature in the current view and its counterpart in adjacent views during sampling, producing a gradient map that guides denoising. Additionally, to miti- gate potential biases and maintain regional continuity, we extend the reference pixel point into a block.
Despite notable progress in multi-view diffusion models, they continue to struggle with issues of poor quality and inconsistencies across different perspectives as shown in Fig. and Fig. 5. These issues primarily stem from the extensive resources required for training, the incomplete- ness of training datasets, and inaccurate correspondences between 3D spatial points and their projections across views. Consequently, it is crucial to delve deeper into efficient 3D asset generation method that accurately captures the real correspondences. To this end, we devise an training-free method, namely MIRROR, to amend the multiple images produced by multi-view diffusion models during sampling process, thereby providing consistent results to perform high-quality 3D reconstruction. We showcase remarkable rectification results in Fig. 1.
The core of MIRROR lies in modifying the current view- point based on the content of adjacent viewpoints, thereby enforcing neighbor-view consistency and naturally deriving whole-view coherence. To achieve effective rectification, we approach this from the following two aspects: (1) What should we focus on in neighbor views for rectification? Compared to image-level adjustments, we opt for point-to- point corrections, ensuring that the features of each physical point remain consistent across different viewpoints. The main challenge of our approach lies in effectively tracking the image coordinates of the same physical point across neighbors. To tackle this, Trajectory Tracking Module (TTM) is proposed to successfully track the motion trajec- tory of physical points viewpoints change. This strategy enables precise adjustments for each point, selectively tar- geting relevant features while disregarding irrelevant ones, which is a key advantage of TTM over the epipolar corre- spondence methods (Ye et al., 2024; Kant et al., 2024; Zhou & Tulsiani, 2023; Li et al., 2024b). (2) How to perform rectification effectively and efficiently? We observe that cross-view attentions (Shi et al., 2023b; Li et al., 2024b) inherently struggle to enforce 3D consistency, as they lack
Technically, we propose a two-stage inference strategy. In the first stage, we generate a set of images using the multi- view diffusion model to extract depth information for each view. Based on transformations in camera angles, in TTM, we introduce a trajectory tracking operator guided by depth maps to track motion trajectories of 3D physical points from the current view to adjacent views at the fixed eleva- tion. This enables precise localization of the corresponding points in neighboring views. We then utilize DDIM Inver- sion (Song et al., 2020) to progressively add noise to the generated images, transitioning them to intermediate noisy states for the subsequent denoising process. During each sampling step of the second-stage inference, we apply a corrective gradient map based on pixel-level similarity in FRM to refine the denoising direction of the multi-view diffusion model, resulting in images with improved quality and consistency. To address background redundancy caused by surrounding blocks, we implement a dual-anchor feature fusion strategy, which fuses relevant features from adjacent views. This not only identifies meaningful features but also significantly improves computational efficiency.
2

---

MIRROR: Make Your Object-Levvl Multi-View Generation More Consistent with Training-Fre Rectification
Furthermore, gradient-based guidance, as discussed in the prior work (Dhariwal & Nichol, 2021), is known be computationally intensive due to the gradient computation requirements for large neural networks. To mitigate this, we provide a theoretical analysis demonstrating that omit- ting the UNet Jacobian term in the diffusion model during gradient-based denoising guidance introduces negligible error. Experimentally, we validate that this optimization achieves a twofold acceleration in the rectification process without compromising the quality of the generated images. An overview of MIRROR is depicted in Fig. 2.
2.2. Multi-View Diffusion Methods
Recent advancements have extended 2D diffusion models to generate multi-view images for reconstruction. Notable de- velopments include MVDream (Shi et al., 2023b), Viewset Diffusion (Szymanowiez et al., 2023), SyncDremmer (Liu et al., 2023b). However, these methods face challenges like texture ambiguity and high computational costs. To address these, Wonder3D (Long et al., 2024) introduces cross-domain model for generating normal maps, while Zero123++ (Shi et al., 2023a) and One-2-3-45 (Liu et al., 2024) improve texture quality and accelcrate 3D reconstruc- tion. Despite these innovations, quality issues persist due to reliance on limited synthetic data. Video generative models, with their temporal modules ensuring frame consistency, are increasingly favored for multi-view generation over image diffusion models. SV3D (Voleti et al., 2024) and IM-3D (Melas-Kyriazi et al., 2024) enhance 3D generation by lever- aging advanced video diffusion models optimize multi- view synthesis for improved output quality. And VideoMV (Zuo et al., 2024) also develops a consistent multi-view generation model using video generative models, further im- proving multi-view consistency with a 3D Aware Denoising Sampling technique. While these methods improve con- sistency, they lack explicit geometric constraints, entirely relying on learned networks. With limited 3D data, they are prone to local optima, and issues like Janus Problem and content drifting remain at inference. Building on these efforts, we propose MIRROR, an efficient, training-free rectification technique specifically designed to enhance 3D consistency in these dense multi-view diffusion models.
In summary, our contributions are as follows:
We innovatively introduce MIRROR, an efficient, training-free, plug-and-play method that enhances multi-view consistency in 3D asset generation by di- rectly rectifying latent noisy images across views dur- ing the sampling process.
We present TTM based on depth information to pre- cisely ascertain the corresponding positions of points across distinct views, and FRM to eliminate ambiguity by enforcing consistency in the representation of the same physical point across different viewpoints.
Extensive experiments demonstrate that MIRROR con- sistently enhances the performance of various multi- view generator, both quantitatively and qualitatively.
2. Related Work
2.1. 3D Generation
3. Preliminaries
Due the scarcity of 3D data, numerous studies have turned to high-quality 2D diffusion models for 3D generation tasks. A fundamental technique in this domain, Score Distillation Sampling (SDS), introduced by DreamFusion (Poole et al., 2022), utilizes pre-trained 2D models as priors to optimize 3D representations, enabling zero-shot text-to-3D genera- tion without 3D data. Inspired by it, several works adopt this pipeline to optimize a neural radiance field (Milden- hall et al., 2021) to generate 3D assets (Lin et al., 2023; Liu et al., 2023a; Qian et al., 2023; Wang et al., 2024b; Zhu et al., 2023; Huang et al., 2023). Furthermore, DreamGaussian (Tang et al., 2023) and Greasian (Yi et al., 2024) accelerate the optimization process by applying Gaussian Splitting techniques (Kerbl et al., 2023) in Score Distillation Sampling. However, these 2D-lifting methods exhibit crit- ical drawbacks, such as severe multi-view inconsistencies and slow generation speeds, often requiring tens of thou- sands of iterations to produce a single 3D asset. In response to these challenges, recent research on multi-view difffusion models has emerged as a promising direction in 3D gener- ation, wherein the model is capable of generating multiple views of an object in a single inference process.
3.1. Diffusion Models
Diffusion Models (DMs) (Ho et al., 2020) learn a target distribution po(z)), by progressively denoising a standard Gaussian distribution. This process is mathematically repre- sented as: p0(o)) = (Zoor) dx1.T, where I1:7 denotes intermediate noisy samples. In the forward process, DMs iteratively add Gaussian noise e to the clean data 2o, con- trolled by pre-defi variance schedule, formulated as:
Dt Varza + vI 04e, NOO,
(I)
Conversely, in the reverse process, DMs employ a denoiser en, parameterized as a UNet, to predict the noise added at each denoising time step
Furthermore, reduce the computational demands asso- ciated with synthesizing high-resolution images, Latent Diffusson Models (LDMs) (Rombach et al., 2022) imple- ment a pre-trained encoder E of VQGAN to compress im- age xy E #x into low-dimensional latent feature, Za = (xo), where 2g E hxxwxe In addition, LDMs uti-
3

---

MIRROR: Make Your Object-Level Multi-Vieyy Generation More Consistent with Training-Fri Rectification
Stage
Trajectory Tracking Module
Inconsistent!
Input
Nelahboring Corragendence
X T Sample
Pretrained Multi-View Diffusion
Add noise
DDIM Inversion
Depth maps
clew
TTM
diew
Output
Feature Rectification Module
X T Sample with
Pretrained Multi View Diffusion
FRM
W
VC
Recefo)
Stage
Consistent!
hosed Fusion
Figure 3: Pipeline of MIRROR. We employ two-stage sampling strategy. In stage 1, given single image text prompt as input, we employ base multi-view diffusion model to sample a set of images. In stage 2, we first apply DDIM Inversion to convert generated images into the noisy state, and take it as the initial noise for the second stage. Then, Trajectory Tracking Module (TTM) is employed to capture the motion trajectory of physical points from the current view to the adjacent views using the depth maps derived from generated images in stage 1. For Feature Rectification Module (FRM), the block features are fused based dual anchors, (t) and (u). Then, explicitly enforce consistency for the same physical point across neighbor views by calculating the distance between the feature of point and the corresponding block features in adjacent views, and compute the overall gradient map VL, to guide the denoising process for rectification.
lize deterministic DDIM sampling (Song et al., 2020) to efficiently transform random noise into clean data 20 through sequence of discrete time steps from = T to t 1, which is formulated as:
sequence Eq. (2) into:
VI Ti Ea ca( (zt,t)
21+1 = =V74+1
Vot
(3)
/ 10-10+116 E8(Ze,t).
4 vI
The (2,,t)
2t-1 av-11
4. Methods
a
(2)
+ / 11-10-180(26t)
In this section, we propose MIRROR, training-free modi- fier specifically designed to enhance consistency across im- ages generated by multi-view diffusion models. In Sec. 4.1, we systematically outline our approach. Then, we intro- duce Trajectory Tracking Module (TTM) that tracks point transformation correspondences between adjacent views (Sec. 4.2) and proposes a Feature Rectification Module (FRM) that integrates 3D-aware feature information to rec- tify inconsistencies during the sampling process (Sec, 4.3).
This sequence ultimately results in the recovery of the latent clean state which is then processed by the VQGAN decoder to reconstruct a high-fidelity image To = D(2))-
Evolved from classical LDMs (Rombach al., 2022), Multi- View Diffusion Models (MV-DMs) have broadened their fo- from single images handling sequences of multi-view images, essentially treating them like videos. Specifically, consider an image sequence Zo represented in IR Fx xx www.wxwwwwo where F' denotes the number of frames. MV-DMs encode this video into a latent space, represented as (x0))
4.1. Framework of MIRROR
Given pre-trained muti-view diffsuion, we adopt two- stage sampling strategy rectify generated images. In the first stage, the base multi-view model generates set of images åo, from which the depth map Z' Z'bsolute is derived via monocular depth estimation. Subsequently, DDIM In- version is then applied to convert clean images into noisy state 2r, serving as the initial noise for the second stage of the sampling process. each denoising step TTM
where 2g E fxx xxxxxxx
3.2. DDIM Inversion
In contrast to DDIM, DDIM inversion acts as a forward process, where clean data 2 progressively transitions into a noisy state 27, effectively reversing the original sampling

---

MIRROR: Make Your Object-Levvl Multi-View Generation More Consistent with Training-Fre Rectification
tracks point correspondences of the predicted latent images, to 2o(2;; t), across adjacent views with Z uabsolute: Given this, FRM integrates 3D-aware feature information accord- ing to the correspondences from TTM, and computes a pixel-level rectification loss gradient V L to refine the de- noising direction of the predicted noise Eg. Finally, 4 is generated through DDIM sampling with the rectified noise Eg, enhancing the quality and consisteney of the final output. The pipeline of MIRROR is illustrated in Fig. 3, with the corresponding pseudocode provided in Algorithm 1.
Depth Prior Estimation. Since images directly output by multi-view dffusion models typically lack depth informa- tion, we utilize a monocular depth estimation model H to estimate this crucial detail 2 in Eq. (4). However, given the potential for considerable bias in depth estimates derived from noisy images, we feed the clean images ěg that have been denoised by the multi-view diffusion model into H. enabling us accurately assess depth information for each viewpoint, ultimately leading to the creation of a relative normalized depth map Zretative == Hān)) E RF Although the depth is estimated from monocular model, the depth scale factor r remains consistent across views, as each baseline multi-view diffusion model fixes the cam- era distance under circular poses during training. This consistency enables the transformation of relative depth into absolute depth for cross-view alignment, formulated as absoltute Zrelative.
4.2. Trajectory Tracking Module
Pixel- WiseeWWseeTTee Trajectory Tracking Transformation. In theory, there exists a pixel-wise correspondence between two im- ages of an object projected from different viewpoints. We observe that large viewpoint gaps under arbitrary camera trajectories tend to reduce cross-view correlation, resulting in increased reconstruction errors and limited improvement. In contrast, fixing the elevation angle strengthens inter-view correspondence and yields better results by providing more adopt standardized set-
Furthermore, we establish that as the denoising step t de- creases, the trajectory tracking of the predicted images to (x) = Var (Z
VI āg E0(2,, t)) at time step t converges to that of the real images Fp, which is detailed in the following proposition.
related information. Therefore, ting with fixed elevation angle. In this setup, we consider F views captured by camera rotating horizontally around the object in a standardized manner. The circular camera tra- jectories ensure stable and uniform coverage of the viewing space, thereby enhancing view correlation and improving the quality of 3D reconstruction. Furthermore, sampling across multiple elevation settings helps recover occluded re- gions that are invisible from a single elevation. Specifically, this configuration maintains constant elevation angle for the images while distributing the azimuth angles uniformly across 360%. Thus, lct a represent the change in azimuth angle between each pair of adjacent views.
Proposition 4.2. (Convergence Analysis). The trajectory tracking error of Ta is upper-hounded by the error induced by the diffusion model, which is given by
[| (fo(# ()) (To()l) S ((1)vo(z1:)) - soll), (5)
where |1-11 is the L2 norm and O denotes a term of the same infinitesimal order. Moreover, it converges to zero as t + O.
The proof is provided in Appendix B. Next, to leverage TTM during sampling for guiding the denoising direction via pixel-level correspondence, the depth map must be inter- polated to match the resolution of the latent space, resulting in: Z'abeotute B(Zabsatuter h, w). where B represents the bilinear interpolation function. And the visualization results of trajectory tracking based on depth information are provided in Appendix G. Unlike methods (Ye et al., 2024; Kant et al., 2024; Zhou & Tulsiani, 2023; Li et al., 2024b) that integrate epipolar geometry into multi-view attention, MIRROR uniquely employs more precise and efficient point-to-point correspondence tracking strategy, As shown in Fig. 4. we demonstrate that epipolar correspon- dence establishes an inaccurate relationship, thus introduces irrelevant background features, leading to overly smooth rectification that lacks detail and the Janus problem. In -iettierectiffedd
Next, we establish the pixel-wise correspondence on the imaging plane between two differing viewpoints. As the camera angle variation is confined solely to the azimuth direction, this motion can be modeled as the imaging plane rotating around the object along the elevation axis. Conse- quently, the y-coordinate (height) remains constant, while the z-coordinate (width) undergoes a rotation transforma- tion related to the z-coordinate (depth). Let V be the fune- tion space T.2 2R² R), the specific transformation formula defined on V is outlined in Definition 1.
Definition 4.1. The trajectory tracking operator Ta : V -> V. defined by an azimuthal rotation angle a E IR, is for- mulated as Ta (Z(x)) Za (u.), where u- (I. y), Ua = (Fa. V.). The coordinates transformation is given by
contrast, MIRROR rectified images maintain higher fidelity and visual quality.
W 2
W - sin a,
Fa = (x
cos + 2
(4)
Va y,
4.3. Feature Rectification Module
where & represents the depth (distance from the camera center to the object), and W the width of the image.
Feature Scrutiny. To maintain the continuity of pixel-wise feature information and in consideration of potential biases
5

---

MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training-Fres Rectification
Iqpu
Epipoler Gamespnderce
Oure
can be fused a mapping M V + V, where M is
defined by
M(Z
Σ
Wo (z)
(9)
A ghest atting eraugee, ab sase.
VEBua
This approach is strategically designed to distance the fused feature points from irrelevant background and enhance their relevance to the target correction point u.
Figure 4: Comparison of the rectification results between the epipolar correspondence method and MIRROR. For better presentation, the background of each image removed.
Rectification Formulation. At first, We can define the ad- jacent operator Fa and use its L2-norm to measure the difference between two different views as follows.
in depth information estimation, we adopt a more robust approach, which extracts the most relevant corresponding features from adjacent views, thereby delivering more precise direction for consistency rectification. Specifically, for each spatial point in the current view, we select not only its corresponding point ta but also the surrounding 3 x 3 block in adjacent views, making total of nine points, denoted as Bu,, as the feature interaction block for point u. This method is figuratively described as the "looking forward and back" feature scrutiny approach, emphasizing a comprehensive examination of the contextual area.
Definition 4.3. The operator Fa V - V is referred to as the adjacent operator, and is defined as
F - - To,
where I denotes the identity operator on space V. and M and Ta is defined in Eq. (9) and Definition 4.1, respectively, with indicating the composition of operators. Note that the composition operator M T is group homomorphism (de- tailed in Appendix B.4). Then, the correction loss function of the spatial point u is given by:
Feature Information Fusion. As mentioned in the introduc- tion, we directly utilize the predicted map 20 = t2;;))
(.) = = (Z.))( [[I-MoTa) (10)
Or co(2), at
20
.gg E IR Fxhxxxx
(6)
Specifically, for each view i, apply La to the feature map 2 (u: t) at each point u. derived from Eq. (6), for time step t. We use one preceding and one subsequent view as reference neighboring views for adjustments. The loss function is formulated as:
from each reverse step as feature tensors, rather than tracting features from the intermediate layers of the UNet network. Our approach offers a simpler and more effective method of feature extraction, providing superior pixel-level alignment at the same time. Since the interaction block Bu., may contain invalid background information, we employ a feature information fusion method based on both posi- tive and negative anchors. The target correction point of the current view is selected as the positive anchor Rpos (u), while the mean of the four corners serves as the negative an- chor Rneg(Ur (u). We calculate the cosine similarity between points in the feature interaction block Bu. and dual an- chors, Rpoe(ut (u) and Reeg( (u). Subsequently, compute the weighted map W, where higher values indicate closer dis- tance the positive anchor (u) and greater distance from the negative anchor Raag (u):
(u; = Σ JEA,
W3 11 (u; MM)) To
(u; 1)
(11)
where A₁ li 1, 1]1ft} denotes the set of adjacent views for the i-th view, {w,) are the weighted coefficients that w.- = 1, and MM) represents the feature interaction block for the j-th view. Now, we can calculate the gradient of the loss L,(u;)) t) with respect to 2 (u) at each spatial point across all views to get gradient map denoted as Ce E IR Fxhxwxx, We then use the gradient map to rectify the denoising direction (detailed in Appendix B.2) at each time step I, formulated as:
W- mm + softmax (C+( v)) + m m softmax (c- 2i). (7) for all E Bu,, where = denote the combined coeffi- cients satisfying n + m = 1, and (u,, v) refer to
E () +- co(2,, + x(1) VI V.
(12)
where s(t) is the rectification scale with respect to f. Due to the high computational cost and time when calculating the UNet Jacobian deg during gradient computation, we De. theoretically (detailed in Appendix B.3) and experimentally (detailed in Appendix D) show that neglecting the UNet Ja- cobian term causes negligible changes in rectification results. The following theorem provides a specific explanation.
(u, v) = Cosine Similarity (Reos (u), v), C (u, =1 Cosine Similarity (Rneg (u).), v).
(8)
Given the weighted map W, the feature interaction block
6

---

MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training-Fre Rectification
Irput View
Syenmre
(ours)
MVD- WVD-Fusion
(ours)
VideaMV (mage-basee)
+MIRROR (ours)
VideaMV
(text-based)
MIRROR
(ours)
aephtit webriag
tokking jpucssal,
parcied
Figure 5: Qualitative Results. The images at the top multi-view images generated by each baseline, while those the bottom showcase the consistency rectification achieved through MIRROR. For casier observation, some areas with Janus Problem or content drifting are marked with red circles. For visual consistency, we replace the gray background of the generated images from text prompts with a pristine white one.
Theorem 4.4. In the denoising process, the rectification ervor induced by neglecting the UNet Jacobian term, has an upper bound controlled by constant %, formulated
5. Experiments
5.1. Experimental Setup
Baselines. In this section, we conduct experimental eval- uations based on image-based and text-based multi-view generation tasks. For image-based tasks, we choose Sync- Dreamer (Liu al., 2023b), MVD-Fusion (Hu et al., 2024). and VideoMV (Zuo al., 2024) based tasks, we adopt VideoMV as baseline method. Each method provides pretrained multi-view model for generat- ing multiple views from single image or a text prompt.
IV Ct
V. Cell S s,
(13)
Vā
and this error converges Therefore, we can rewrite the rectification formula Eq. (12):
0.
baselines. For text-
VI V 0
Ee(Ze, t) + t0(2),)) + s(t)
(14)
where V Lr denotes the gradient map with respect êp. We demonstrate that Eq. (14) achieves nearly twofold speedup compared to Eq. (12) (Tab. 2), enabling faster rec- tification without sacrificing the quality of the generation. The rectified noise ég is then subsequently fed into Eq. (2) to carry out the sampling procedure utilized in standard DDIM (Song et al., 2020), but with the rectified noise predictions.
Metrics. Building on these baselines, we quantitatively evaluate performance using metrics including PSNR, SSIM (Wang et al., 2004), and LPIPS (Zhang et al., 2018). We also report Clip-Score for both image- based and text-based gen- eration tasks to evaluate the alignment between generated images and the corresponding input.

---

MIRROR: Make Your Object-Level Multi- Generation More Consistent with Training-Fri Rectification
Input
Stagel (baseline)
Stagel + Rectification
Stage2
Stage2 + Rectification (ours)
A horse walking 3D asset.
An elephant wearing real soarf 3p asset.
Figure 6: Ablation Study on the Two-Stage Rectification Process. Results generated using baseline methods often exhibit severe Janus Problem. Attempts to rectify these directly at stage 1, as well as outcomes from a two-stage baseline process, have proven inadequate in resolving these issues. In contrast, our method significantly reduces these inconsistencies.
Implementation Details. We implement MIRROR on top of the baseline models discussed earlier in this section. This integration of the rectification process occurs exclusively during the inference phase, thereby obviating the need for any supplementary training or fine-tuning. Given that TTM relies on depth guidance, we employ the saate-of-the-an monocular depth estimation model, Aoppth-Anyhhnng-Ve V2- Small (Yang et al., 2024), to generate depth information. We also provide an ablation study on alternative depth guidance methods, detailed in Appendix E, and demonstrate that MIRROR's performance improves with the advancement of depth estimation models. Following baselines, we use NeuS (Wang et al., 2021) for 3D reconstruction. Although calculating the UNet Jacobian in Eq. (12) is time-consuming and significantly increases inference time, our method, as described in Eq. (14), shows that by omitting this term and integrating MIRROR into the baseline models, the inference time is considerably reduced. As shown in Tab. 2, our two- stage sampling process is completed in just few minutes. Additional experimental details are provided in Appendix C.
Table I: Quantitative Comparison with Baselines, Image- based and text-based generation results are evaluated on GSO (Downs et al., 2022) and Bench (He et al., 2023) datasets, respectively.
Method
PSNR 1 SSIM f LPIPS ClipS f
SyncDreamer
18.75
0.739
0.198
76.60
+MIRROR (ours)
19.37
0.794
0.172
78.28
MYD--Fuiio
18.90
0.785
0.172
68.66
+MIRROR (ours)
20.15
0.801
0.168
71.52
VideoMV (image-based)
17.64
0.755
0.197
71.77
+MIRROR (ours)
18.26
0.793
0.176
73.30
VideoMV (text-based) +MIRROR (ours)
21.67
0.814
0.206
26.96
24.82
0.898
0.115
32.78
enhances consistency but also significantly improves quality and geometric fidelity, clearly surpassing all baseline results. More results are provided in Appendix K.
Quantitative Analysis. The quantitative results in Tab. I are in strong concordance with the visualization outcomes. Incorporating MIRROR significantly boosts 3D consistency- related metrics (PSNR, SSIM, and LPIPS) over all baseline methods, clearly demonstrating notable enhancements in consistency and quality for multi-view generation. More- over, calculate the average CLIP score between gen- erated images and the input, and we observed marked increase in it, indicating stronger aligmment between the generated content and the input. Moreover, 3D reconstruc- tion metrics are also reported Appendix C.4.
5.2. Comparison with Baseline Methods
We implement MIRROR across diverse open- soer-s multi- view diffusion models and carry out both quantitative and qualitative comparisons.
Qualitative Analysis. For image-based generation, Fig. clearly illustrate that although baseline models can produce plausible views from angles near the input perspective, they exhibit inconsistencies (Janus Problem and content drift) in lateral and rear views, along with undesirable indentations, and misalignments. Fig. 5 shows that text-based genera- tion of VidcoMV also suffers from low-quality outcomes with undesirable artifacts and inconsistencies, such as an 'elephant' with multiple trunks and missing legs, and "chameleon" without head. By comparison, incorporat- ing MIRROR into these basclines successfully resolves the prominent artifacts and multi-face issues encountered in baselines, as observed in Fig. and Fig. 5, which not only
Inference Time. Our experiments are conducted using a single NVIDIA L40S GPU, with the two-stage sampling process completing in just few minutes. Notably, incorpo- rating MIRROR into the baseline models does not result in substantial increase in computational load or processing time. We test the inference time of the baseline methods and the methods with MIRROR applied, as shown Tab. 2. Although the computation of the UNet Jacobian is time-
8

---

MIRROR: Make Your Object-Level Generation More Consistent with Training-Fre Rectification
Table 2: Inference Time of Baseline Models and Their MIRROR-Enhanced Versions. The time multiples relative to the baseline are shown in parentheses.
MIRROR
MIRROR (ours)
Models
Baseline
(w. UNet Jacobian)
(w/o UNet Jacobian)
SyncDreamer
16s s
91 (x5.7)
43 s (x2.7)
MVD-Fusion
20 S
112 S (x5.6)
52 s (x2.6)
VideoMV (image-based)
22 s
103 (x4.7)
64 s (x2.9)
VideoMV (text-based)
34 s
154 (x4.5)
98 (x2.9)
Input
Baselina
w/l Depth Guidance
Ours
ROR utilizes depth information from the initial denoising stage for more accurate tracking and rectification in the subsequent stage, which significantly improves multi-view consistency and model fidelity, as shown in the final column.
A pou of
seae:
warting rol sterffal ai asst.
Depth Guidance. Within TTM, our approach leverages depth information to obtain more accurate spatial corre- spondences between different viewpoints. However, in the absence of depth guidance (i.e., 2 = 0 in Eq. (4)), it fails to precisely establish correspondences between adjacent views, leading to unsatisfactory results, as depicted in the second column of Fig. 7, with the missing head in the first row and content drift problem of 'red scarf' in the second row.
Figure 7: Ablation Study on Depth Guidance, TTM without depth guidance exhibits errors in correspondence, leading to unsatisfactory and inconsistent results.
Inpul
No anchors
Only postive anchors
Only ragative anchers
Ours
Dual-Anchor-Bast Fusion. To validate our method's 's ef- fectiveness and generalizability, we conducte ablation stud- ies on multi-view generation against white and gray back- grounds. Our findings, detailed in Fig. 8, show that moving away from dual-anchor approach to using average pool- ing or single-anchor feature fusion leads to various issues. Specifically, omitting the anchor-based approach results in excessive saturation and discontinuity in details, while using only positive anchors improve local feature precision but is prone to background interference. Sole reliance on negative anchors minimizes distractions but lacks focus on essential target characteristics. Conversely, our integrated approach, which combines positive and negative anchors, substantially enhances relevant detail attention and consistency, thereby outperforming other methods. More ablation study results are provided in Appendix J.
ploto
Figure 8: Ablation Study on Dual-Anchor-Basee Fusion. Alternative methods yield suboptimal results, while the dual- anchor-based method (ours) markedly reduces background interference and improves the continuity of local features.
consuming (w. UNet Jacobian), increasing the inference time by 4.5 to 5.7 times compared to the baselines, our method demonstrates that the UNet Jacobian term can be omitted, leading to a significant reduction in inference time, with only a 2.6 to 2.9 times increase (w/o UNet Jacobian). This computational cost is still minimal when compared to training- based methods, enabling the generation of high- quality dense images within minutes.
6. Conclusions
In this paper, we introduce MIRROR, a novel plug-and-play technique that effectively resolves inconsistencies in 3D geometry and artifacts in diffusion-based multi- view gener- ation without training. Using trajectory tracking and feature rectification modules, MIRROR successfully enhances the photorcalism of synthesized images across views. Gen- eralization evaluations confirm MIRROR's compatibility with various multi-view diffusion models, making it uni- versal correction tool that markedly improves generation consistency and fidelity, thereby delivering higher-quality multi-view view images for 3D asset reconstruction.
5.3. Ablation Study
Two-Stage Rectification Process. Fig. 6 highlights that replacing our two-stage rectification process with a single- stage correction (the second column) fails to resolve the Janus problem evident in baselines, as inaccuracies in depth extraction lead to deformed 'horse" and 'elephant' models with missing or unrealistic features. And relying solely on a two-stage denoising process without rectification (the third column) still leads to inconsistencies. Conversely, MIR-

---

MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training-Fre Rectification
Acknowledgements
Kant, Y., Siarohin, A., Wu, Z., Vasilkovsky, M., Qian, G., Ren, J., Guler, R. A., Ghanem, B., Tulyakov, S., and Gilitschenski, I. Spad: Spatially aware multi-view dif- fusers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10026- 10038, 2024.
This paper is supported by National Key R&D (Research and Development) Program of China the National Natural Science Foundation of China (Nos. 12431012, U23B2012, 12471308) and Beijing Natural Sci- ence Foundation (1254050).
Karaev, N., Makarov, I., Wang, J., Neverova, N., Vedaldi, A., and Rupprecht, C. Cotracker3: Simpler and better point tracking by pseudo-labelling real videos. arXiv preprint arXi::2410.1188) 2024.
Impact Statement
This project aims to provide an effective, training-free tool for improving the generative quality of multi-view diffu- sion models. Our approach enhances the 3D consistency of multi-view images while simultancously improving the overall quality of the generated content. However, similar to other generative technologies, there exists a potential for misuse. Malicious entities could combine this method with targeted generative models to produce misleading or harm- ful 3D content. Addressing these ethical considerations is critical, and future research in 3D generation must priori- tize understanding and mitigating these risks to ensure the responsible application of such technologies.
Kerbl, B., Kopanas, G., Leimkühler, T., and Drettakis, G. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 20233
Langley, P. Crafting papers on machine learning. In Langley, P. (ed.), Proceedings of the 17th International Conference on Machine Learning (ICMI. 2000), pp. 1207-1216, Stan- ford, CA, 2000. Morgan Kaufmann.
Li, B.,, Zhang, Z.,, Yang, X., and Wang. X. Focus on neigh- bors and know the whole: Towards consistent dense multi- view text-to-image generator for 3d creation, 2024a. URL https: / httpaaaxvvivv.. org/ abs / 2 208 13149.
References
Li, P., Liu, Y., Long, X., Zhang, F,, Lin, C., Li, M., Qi, X., Zhang. S., Luo. W., Tan, P., et al. Era3d: High-resolution multiview diffusion using efficient row-wise attention. arXiv preprint arXiv:2405. 1616, 2024b.
Dhariwal, P. and Nichol, A. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780-8794, 2021.
Downs, L.. Francis, A., Koenig, N., Kinman, B., Hickman, R., Reymann, K., McHugh, T. B., and Vanhoucke, V. Google scanned objects: A high high-quality dataset of 3d scanned household items. In 2022 International Confer- ence on Robotics and Automation (ICRA), pp. 2553-2560. IEEE, 2022.
Lin, C.-.,, Gao, J., Tang. L.. Takikawa, T., Zeng, X., Huang, X., Kreis, K., Fidler, S., Liu, M.-Y., and Lin, T-YY Magic3d: High-resolution text-to-3d content cre- ation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 300-309, 2023.
Liu, M., Xu, C., Jin, H., Chen, L.,, Varma T, M., Xu, Z., and Su, H. One-2-3-45. Any single image to 3d mesh in 45 seconds without per-shape optimization. Advances in Neural Information Processing Systems, 36, 2024.
He, Y., Bai, Y., Lin, M., Zhao, W., Hu, Y., Sheng, J., Yi, R., Li, J., and Liu, Y.-J. T bench: Benchmarking cur- rent progress in text-to-3d generation. arXiv preprint arXiv:2310.02977.222 2023.
Liu, R., Wu, R., Van Hoorick, B., Tokmakov, P., Zakharov, S., and Vondrick, C. 7ero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pp.
Ho. J., Jain, A., and Abbeel, P. Denoising diffusion proba- bilistic models. Advances in neural information process- ing systems, 33:6840-688 6851, 2020.
2023a.
Liu, Y., Lin, C., Zeng, Z., Long, X., Liu, L., Komura, T., and Wang, W. Synedreamer: Generating multiview- consistent images from a single-view image. arXiv preprint arXiv:2309.03453 2023b.
Hu, H., Zhou, Z., Jampani, V., and Tulsiani, S. Mvd- fusion: Single-view 3d via depth-consistent multi-view generation. In Proceedings of the IEEF/CVE Confer- ence on Computer Vision and Pattern Recognition, pp. 9698-9707, 2024.
Long, X., Guo, Y.-C., Lin, C., Liu, Y., Dou, Z., Liu, L., Ma. Y., Zhang, S.-H., Habermann, M., Theobalt, C., et al. Wonder3d: Single image to 3d using cross-domain dif- fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9970 9980, 2024.
Huang, Y., Wang, J., Shi, Y., Xi, Zha, Z.-J., and Zhang, L. Dreamtime: An improved optimization strategy for text- to-3d content creation. arXiv preprint 2xXiv22306.12422, 2023.
10

---

MIRROR: Make Your Object-Level Multi- View Generation More Consistent with Training-Fre Rectification
Melas-Kyriazi, L., Laina, I., Rupprecht, C., Neverova, N., Vedaldi, A., Gafni, O., and Kokkinos, F. Im-3d: Iterative multiview diffusion and reconstruction for high-quality 3d generation. arXiv preprint arXiv:2402.08682, 2024.
Wang, H., Du, X., Li, Yeh, R. A., and Shakhnarovich, G. Score jacobian chaining: Lifting pretrained 2d dif- fusion models for 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12619-12629, 2023.
Mildenhall, B., Srinivasan, P., Tancik, M., Barron, J. Ramamoorthi, R., and Ng. R. Nerf: Representing scenes as neural radiance fields for view synthesis. Communica- tions of the ACM, 65(1))99-100 2021.
Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., and Wang, W. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2 10689, 2021.
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B. Dream- fusion: Text-to-3d using 2d diffusion. arXiv preprint MXXi::2009.19888 2022.
Wang, S., Leroy, V., Cabon, Y., Chidlovskii, B., and Revaud, J. Dust3r: Geometric 3d vision made easy. In Proceedings of the IEEE/CVE Conference Computer Vision and Pattern Recognition, pp- 20697-20709, 2024a.
Qian, G., Mai, J., Hamdi, A., Ren, J., Siarohin, A., Li, B., Lee, H.-Y., Skorokhodov, I., Wonka, P., Tulyakov, S., et al. Magic123: One image high-quality 3d object generation using both 2d and 3d diffusion priors. arXiv preprint arXiv:2306. 17843, 2023.
Wang, Z., Bovik, A. C.,, Sheikh, H. R., and Simoncelli, E. P. Image quality assessment: from error visibility struc- tural similarity. IEEE transactions on image processing, 13(4):600-612, 2004.
Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., and Koltun, V. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-datased transfer. IEEE Iransactions pallern analysis and machine intelligence, (113)162231637, 2020.
Wang, Z., Lu, C., Wang, Y., Bao, F., Li, C.,, Su, H., and Zhu, J. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. Advances in Neural Information P Processing Systems, 36, 2024b.
Rombach, R., Blattmann, A.. Lorenz, D., Esser, P., and Ommer, B. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF con- ference computer vision and pattern recognition, pp. 10684-10695, 2022.
Yang, L., Kang, B., Huang, Z., Zhao, Z., Xu, X., Feng, J., and Zhao, H. Depth anything v2. arXiv preprint MXXiv:440.00911111 2024.
Ye, J., Wang, P., Li. K., Shi, Y., and Wang. H. Consistent-l- to-3: Consistent image to 3d view synthesis via geometry- aware diffusion models. In 2024 International Confer- ence 3D Vision (3DV), pp. 664-674. IEEE, 2024.
Shi, R., Chen, H., Zhang. 7.,, Liu, M., Xu, C.,, Wei, X., Chen, L., Zeng, C., and Su, H. ++:o single image to consistent multi-view diffusion base model, 2023a.
Shi, Y., Wang, P., Ye, J., Long, M., Li, K., and Yang, X. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint ar 230: /65/2, 2023b.
Yi, T., Fang. J., Wang, J., Wu, G., Xie, L., Zhang, X., Liu, W., Tian, Q., and Wang, X. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6796-6807, 2024.
Song. J., Meng, C., and Ermon, S. Denoising diffusion implicit models. arXiv preprint wwXiv:2010002502, 2020.
Szymanowicz, S., Rupprecht, C., and Vedaldi, A. Viewset (iffusion:((-) image-conditioned 3d generative models from 2d data. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pp. 8863-8873, 2023.
Zhang, R., Isola, P., Efros, A., Shechtman, E., and Wang, O. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 586-595, 2018.
Tang, J., Ren, J., Zhou, H., Liu, Z., and Zeng, G. Dream- gaussian: Generative gaussian splatting for efficient 3d content creation. arXiv preprint arXiv:2309. 16653, 2023.
Zhou, Z. and Tulsiani, S. Sparsefusion: Distilling view- conditioned diffusion for 3d reconstruction. In Proceed- ings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 12588-12597, 2023.
Voleti, V., Yao, C.-H., Boss, M., Letts, A., Pankratz, D., Tochilkin, D., Laforte, C., Rombach, R., and Jampani, V. Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion. arXiv preprint ar Xiv:22403. /2008, 2024.
Zhu, J., Zhuang, P., and Koyejo, S. Hifa: High-fidelity text-to-3d generation with advanced diffusion guidance. arXiv preprint arXiv:2305. 18766, 2023.

---

MIRROR: Make Your Object-Levvl View Generation More Consistent with Training- Free Rectification
Zuo, Q. Gu, X., Qiu, L., Dong. Y., Zhao, Z., Yuan, W., Peng, R., Zhu, S., Dong, Z.,, Bo, I.., et al. Videomv: Consistent multi-view generation based on large video generative model. arXiv preprint arXiv:2403 2024.
12

---

MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training-Fre Rectification
A. Appendix Overview
In the appendix, we provide the following supplementary material to support the main text:
Derivation and Proofs (Appendix B);
Additional Implementation Details (Appendix C);
Convergence and Stability Analysis of MIRROR (Appendix D);
Depth Guidance (Appendix E);
Discussion with Multi- View Depth Estimation (Appendix F);
Correspondence Visualization of Trajectory Tracking Transformation (Appendix G);
Discussion with Point Tracking Method (Appendix H);
Limitations and Future Work (Appendix I);
More Ablation Studies (Appendix J):
More Results (Appendix K);
Failure Cases (Appendix L).
B. Derivation and Proofs
B.1. Proof of Proposition 4.2
Proof. Based Definition 4.1 of TTM, the trajectory tracking error between the predicted image ât (x, t) and the true image can be scaled as:
(folaae 1)) Ta (ax) s ((o,(((( ua (xo)11 s (1))) f)) H(xx))12
(15)
Using the first-order Taylor expansion, we obtain:
H(xx) H(ao(a,, f)) + (=(2z)) So(21, ()) o(1 - fo( t)l),
(16)
where represents higher-order infinitesimal. And 7. is pretrained ViT network with a continuous, bounded gradient that outputs scale-consistent absolute depth, showing that the depth-level error is of the same order as the pixel-level error:
( 6)) H(xo) || [[VH(z)) f0 t)) O(I 5o( 1).)
(17)
Then, substituting Eq. (17) into Eq. (15) results in the following incquality:
[TT((((()) Ta (xo)1 O(()oo(ae.)) zoll3).
(18)
Moreover, note that the right side of the inequality Eq. (18) is equivalent to the Kullback-Leibler (KL) divergence between forward process posteriors g and reverse process posteriors estimation pe diffusion process:
Oi1 1) æa ) KI (10
(19)
since both terms represent the training objective of the diffusion model. Therefore, the inference process, the trajectory tracking error is controlled by the diffusion model, which has a finite upper bound. Besides, as t decreases, the predicted image do (2v, approaches the clean image Zo. causing the error to converge to zero.
13

---

MIRROR: Make Your Object-Level View Generation More Consistent with Training-Fri Rectification
B.2. The Derivation of the Rectification Formula Eq. (12)
According to Bayes' Theorem, the objective function can be decomposed into the following two terms:
Vx, (=x where c denotes the azimuth angles of the cameras in the multi-view images. Furthermore, since pe is a Gaussian distribution, the transformation relationship between the score function and the denoiser of the diffusion model can be easily derived, i.e.
(c) V pog) + Vz, (c)=x),
(20)
log po(z) vI
EO(2),
(21)
According to Eq. (20) and Eq. (21), we can derive the following expression:
E0 t, c) Eo (2+, t) vI 04 v., log po((p()))))
(22)
By substituting the exponential form of the loss function, exp( into Eq. (22), the rectification formula of the denoising direction is derived as follows:
éø(z,, c) + sa(z,, 1) + s(t) v1 Te Ct,
(23)
where st) is the rectification scale with respect to t. For brevity, Eo(2,, t, c) is denoted as (0(2,, t) in the main paper.
B.3. Proof of Theorem 4.4
Proof. First, by applying the chain rule, the gradient V Ci can be expanded as follows:
dān
Den
V ft
V
v1
at
V Lt,
(24)
d2t
v
Crt
deg being the UNet Jacobian of the diffusion model. When the UNet Jacobian term is neglected, the gradient error with De. between the two terms can be expressed as:
V C.
Cxe
DEH dz,
V C.
(25)
Note that as decreases, the noise (xt,,1 t) becomes increasingly independent of the data distribution, causing the UNet Jacobian term to converge to as t - 0. Then, dee ||
10 Cil + 0 as - 0. Therefore, there exists constant y such
that
D5 dea 22, S y, for all L. Moreover, since the coefficient -- E [0, 1]. the gradient error of Eq. (25) has finite
Voc
upper bound.
Furthermore, after individually rectifying the noise estimation based on the two gradients in Eq. (12) and Eq. (14), respectively, the error of 2 obtained through sampling process also converges to zero as t decreases. The detailed derivation process is as follows:
(z))
2 2/-0
(x))
(20)
a-11
21
at
+
/1 11-10-11 c(())
αt-1
C2.
Ci (20)
21 1
/ To- E
(50)
a1-1
Vai
v
10-1V1
ae
/1 - a
(((2)))
E
($a)
Va:
Vai-
a+))
=s(()
v - 101-0,111 a1-1)(11 a.)
V Le
V Lr
Vai
var
V1 ai
Va
- σx)
DEe
(1)
v(1 - T (1 Tr
V 20
Va,
Va
Øz
Att),,),
(26)
where the superscripts (zx) and (Eo) correspond to the two gradient correction methods in Eq. (12) and Eq. (14), respectively.
And the coefficient A(1) st)
v1-a.
/o (a))
(1
0. 1) (T) 0 as time step / - 0.
Va.
/ Va.
14

---

MIRROR: Make Your Object-Level Multi- View Generation More Consistent with Training-Free Rectification
Algorithm 1 Framework of MIRROR
Input: a single image or a text prompt
Pretrained baseline model: B.M
Parameter: rectification scale s(t) with respect to t
Output: multi-view consistent images 20
2r t sample from N0 I)
2: 2t + 2T-
3: for = -,, ..... , do
4:
4 -B,((z),C,)). 5: 21-1 + (DD
6: end for
7: 8o x D(zo).
8: abbsoute & DEPTH
9: 2T DDIM (B,,,, C),
10: 2t + ZT.
11: for = =T,T- - - do
12:
+ (B,,,,,,,tt
13: C, < [Fa(zt, 2 Zabsolute) )13 112.
14: Eo + Eo + s(t) VI-aa Vio.r
Voa
15: 21-1 + DDIM EB)
16: end for
17: do + (200)
18: return Zo
B.4. Group Homomorphism Property of Operator M Ta
To prove this property, we first establish the following lemma.
Lemma B.1. The trajectory tracking operator To is group homomorphism. Specifically, for any or,, 02 E R, it holds that: Ta, Ta =Ta+tar
Proof. is important to note that Ta represents a rotation operator of angle around the y-axis within the special orthogonal group SO(3), which can be expressed using the matrix exponential of the Lie algebra element associated with the rotation group. Specifically, it is given by:
0 0
-1
Ta = exp(aA), =
(27)
By leveraging the properties of the matrix exponential, we observe that the composition of two such rotation operators results in new rotation operator. That is, the combination of two rotations is given by:
Tap Tog exp(ar A) exp((22) exp((a) + a2) To1+02-
(28)
Therefore, the rotation operators Ta are closed under the operation of parameter addition, demonstrating the group property of (3(3) with respect the rotation angle.
Proposition B.2. For any 01, 02 R, the composition of two operators M Ta, and M Tag adheres the following operational relation:
(Mo (M) (M Tag) = M o Taitag-
(29)
In other words, M To is group homomorphism.
Now, we proceed to prove Proposition B.2.
15

---

MIRROR: Make Your Object-Level Hulti-Viey Generation More Consistent with Training-Fre Rectification
A pig wearing backpack, 3D asset.
Input
T-10
T-20
T=30
T=40
T=50
Figure 9: Ablation Study on the Step T in DDIM Inversion. When T is too small, noise disrupts the sampling; too large, it increases inference time. Therefore, after balancing inference time and generation quality, we set I = 50
Proof. It is important to observe that, since the operator Ta is solely dependent on the coordinates of the spatial points and is independent of the feature values, it can be deduced that
(M Ta,) (Mo To2) = Mo (Ta, Taa) M Ta +02
(30)
where the second equality is derived from Lemma B.I.
C. Additional Implementation Details
C.1. Feature Rectification Module
In Algorithm 1, we systematically present the pseudocode for the entire rectification algorithm. Below are the parameter
selection strategies for several key parameters used in FRM. First, we have determined the weights for the fusion of positive
and negative anchors at (m=+ = 0.5, m = 0.5) based on empirical observations. Fig. 8 shows the (m = 1, m = 0) would cause grayish or bright tones and (m+ = 0, m 1) may fail to focus on the current point, leading to poor results. To maintain general applicability, we chose (m+ = 0.5, m 0.5) as the robust option. Second, we define the rectification scale s()) match the consistency gradient magnitude with Ey of each baseline. As geometric prototypes primarily develop during the initial sampling stages of the inference process, with subsequent stages dedicated refining detailed textures, it is essential to focus the early phases since aim to adjust geometric shapes to address inconsistencies such as Janus Problem. In the later stages, we intentionally decrease the correction scale to prevent overcorrection. Besides, based on experimental observations, the first five denoising steps contain excessive noise, and applying the correction algorithm during this stage leads to redundant information. Therefore, we start applying our correction algorithm after the fifth step. Technically, we define the rectification scale stt) as a gradient function dependent on Its specific form is given by Eq. (31) for Sremmre and HVD-Fusion, and by Eq. (32) for VideoMV, and can be further adapted accommodate different models.
0.002, t E 450,
0.005, E (40, 45).
0.008, E (30, 40).
0.024,
E (30, 40).
st))
0.005,
ee 30),
(31)
s(t)
0.015,
t E (20, 30).
(32)
0.003,
t E (10,20),
0.008, t E (10, 20].
0.001,
10).
0.003, E [1, 10).
Furthermore, adjustments per model are feasible. Third, the weights {wj} in the rectification formulation of the adjacent views, both preceding and succeeding, are assigned randomly, as the feature information provided by these two neighboring acknowledge that camera parameter noise is unavoidable. but its
views is considered equally important. Additionally, impact is minimal since the baselines were trained with standard parameters. To alleviate this, we employed block-based trajectory tracking, using a 3 x 3 block to fuse the corresponding features. While we experimented with larger windows, it introduced excessive redundant information, ultimately affecting performance. Additionally, increasing the window size further increased the computational load. As a trade-off between computation time and generation quality, we selected the 3 x block size.
16

---

MIRROR: Make Your Object-Levvl Multi-View Generation More Consistent with Training-Fre Rectification
C.2. The Step of DDIM Inversion
The number of DDIM sampling and inversion steps both set to T' 50. The number of DDIM sampling steps in the two-stage process is kept the same as the baselines. And have tried with different T for DDIM Inversion. As shown in Fig. 9, when T' is too small, the added noise becomes excessively coarse, negatively impacting the denoising process in the second stage. Conversely, when T is too large, it significantly increases the overall inference time. Therefore, we have settled on T = 50, which strikes a balance between maintaining high quality and ensuring computational efficiency, consistent with the inference process steps.
C.3. Evaluation Datasets
For image-based generation, we evaluated 100 objects from Google Scanned Objects (GSO) (Downs et al., 2022) dataset, using the 16, 16, and 24 views provided by SyneDreamer, MVD-Fusion, and VideoMV, respectively. Following text- batt-basdd generation in VideoMV, we used 100 single-object prompts from Bench (He et al., 2023), sampling half of the equidistant views (12 views) for neural field reconstruction, employing them as pseudo ground truth for quantitative comparisons.
C.4. 3D Reconstruction
We follow the baseline SyneDreamer (Liu et al., 2023b) to utilize NeuS (Wang et al., 2021) for 3D reconstruction. Specifically, the training process for one object consists of 10,000 steps with a warm-up of 100 steps and loss weights Argb 0.5, Amask 1.0, Xeikonal 0.1. We provide the commonly used Chamfer Distance (CD) and Volume IoU between ground-truth shapes and reconstructed shapes in Tab. 3 to further validate the effectiveness of our method in improving the quality and consistency of both multi-view images and 3D reconstruction.
Table 3: Quantitative Comparison with Baselines on 3D Reconstruction Metrics. Image-based and text-based generation results are evaluated on GSO and Bench datasets, respectively.
Method
Chamfer Distance | Volume IoU
SyncDreamer
0.042
0.514
+MIRROR (ours)
0.039
0.530
MVD-Fusion +MIRRRR (ours)
0.033
0.602
0.029
0.656
VideoMV (image-based)
0.031
0.643
+MIRROR (ours)
0.026
0.667
VideoMV (text-based)
0.046
0.538
+MIRROR (ours)
0.028
0.626
D. Convergence and Stability Analysis of MIRROR
We experimentally validate the convergence and stability of our algorithm on baseline methods. Firstly, as shown in Fig. 10 (a), we observe that as decreases, the predicted noise Ég rectified by MIRROR steadily decreases. Second, Fig. 10 (b) and (c) demonstrates that both the loss gradient error of Fq. (25) and the error about 2, during one-step DDIM sampling of Eq. (26), which are caused by neglecting the UNet Jacobian term, gradually converge to zero as decreases. Furthermore, Fig. 11 demonstrates that both rectification formulas effectively produce consistent multi-view images. Therefore, by combining the results in Fig. 10 and Fig. 11, we can conclude that the error introduced by omitting the UNet Jacobian term negligible. Therefore, using Eq. (1 (14) as the rectification formula is more efficient and reasonable strategy.
E. Depth Guidance
We evaluated the performance of the current state state-of-the-art monocular depth estimation method, et al., 2024) and an alternative method, MiDaS (Ranftl et al., 2020). Experimental results demonstrate that Depth-Anything- V2 is the optimal choice for depth estimation the TTM framework.
(Yang
17

---

MIRROR: Make Your Object-Level MultiiVieey Generation More Consistent with Training-Fre Rectification
Videoety (image-based)) SyncDreamer MUD-Fussion
Viseo nesst-based)
Vicco (Imago Based)
Symcbreame
horm Prediced Notee Hote
2.30
0.0025
69023
Lo Gredent trrer
Loss of 0 Error
2.08
2.000
2.00
0.001
20025
0.0500
50223
VideoWf
SyncDroamer RVD-luaion
60015
0.000
20
T-t
T-1
T-t
(a)
(b)
(c)
Figure 10: Convergence and Stability Analysis of MIRROR. (a) The trend predicted noise és variation during the inference process with MIRROR; (b) The convergence of gradient emrors in Eq. (25); (c) The convergence of one-step DDIM sampling errors in Eq. (26) when omitting the UNet Jacobian term during the inference process.
W. Unet Jacobian
w/o Unet Jacobian
Figure 11: Comparison of generated results with (w.) and without (w/o) considering the UNet Jacobian term, generated from VideoMV (Zuo et al., 2024) rectified with MIRROR. Both rectification formulas are capable of generating multi-view images with strong consistency.
First, we evaluate the eтror of Eq. (5) for depth estimation in TTM using three image-based baseline models, VideoMV (Zuo et al., 2024). SyncDreamer (Liu et al., 2023b) and MVD-Fusion (Hu et al., 2024), with Depth-Anything-v? on GSO dataset. Fig. 12 (a) demonstrates that during the denoising process, the trajectory tracking error minimal with the depth guidance from Depth-Anything V2 (DA2), and as f decreases from T to 0, gradually converges to zero. Besides, Fig. 12 (b)-(d) show the comparison of depth estimation errors for each baseline model using DA2 (blue) or MiDaS (orange). In particular, in Fig. 12 (d), for MVD-Fusion, also utilize the depth values predicted by its pretrained model facilitate TTM, which serves the reference standard (green). It is noteworthy that the depth estimation error of DA2 is the smallest among all the baseline methods.
Moreover, we also evaluate the qualitative results (Fig. 13) and quantitative metrics (Tab. 4) on VideoMV (Zuo et al., 2024) rectified with MIRROR. using different monocular depth estimation models for guidance. We found that both the absence of depth estimation and the use of the alternative MiDaS model introduced significant errors to TTM, resulting in 3D inconsistency in the generated multi-view images. In contrast, when using DA2 for depth estimation, the generated results more consistent and achieved the best performance across all quantitative metrics.
F. Diseussion with Multi- Depth Estimation
We replaced Septh-Anything. V2 (DA2) with the multi-view depth estimator, DUS:3R (Wang et al., 2024a). As shown in Tab. 5, DUSt3R does not significantly increase inference time but requires more memory, whereas DA2 incurs minimal overhead. In low-memory environments (e.g., a single NVIDIA 3090 GPU), DUSt3R fails to run. While DUSt3R slightly
18

---

MIRROR: Make Your Object-Level Multi- View Generation More Consistent with Training-Free Rectification
0.000
Syna mummu [UAJ)
(41165)
0.00
MVD-Fusion (DA2)
0.0010
Extimation
Extimation
0.0030
0,008
0.0077
T-t
T-t
(a)
(b)
SyncDreamer
0.000
Rusien (M
MVD-Rusien (ltel))
of Da Extimatin
12 LL of Dipto Extimation
0.09
0.004
0.000
0,0
0.000
0.007
0.09
T-t
T-t
(c)
(d)
Figure 12: Convergence Analysis of Depth Guidance Models. (a) shows that the trajectory tracking error is minimal with Depth-Anything- V2 (DA2) guidance, converging to zero decreases. (b)-(d) compare the depth estimation errors of each baseline model using the depth values predicted by its pretrained model to facilitate TTM, which is set the reference standard (green).
(DA2, blue) and MiDaS (orange). In (d), for MVD-Fusion, additionally
Input A bright blue plastic swin.wing goggles. sa asset.
Baseline
w/o Depth Guidance
Depth-Anything-v2 (Ours)
paint splattered eosel, SD asset.
Figure 13: Ablation Study on Depth Guidance Models: MiDaS vs. Depth-Anything-Ve V2.
improves PSNR and LPIPS, SSIM and CLIP Score remain comparable to DA2, Visual results in Fig. 14 show marginal improvement, but in some cases, DUSt3R underperforms DA2.
In summary, multi-view depth provides modest gains but with memory trade-offs. As depth estimation is independent of our core contribution, these results along with those in Appendix show that MIRROR can benefit from ongoing advances depth estimation.
19

---

MIRROR: Make Your Object-Level Multi- Generation More Consistent with Training-Fre Rectification
Table 4: Quantitative Ablation Study on Different Depth Guidance Methods.
Ablation Study
PSNR
SSIM
LPIPS & ClipS f
Baseline
21.67
0.814
0.206
26.96
w/o Depth Guidance
16.13
0.757
0.369
26.51
MiDaS
22.41
0.863
0.258
25.79
Depth-Anything-V V2 (ours)
24.82
0.898
0.115
32.78
Input
Baseline
DUS:3R
Ours
A sleek stainless steel teapot, 3D asset.
bear dressed Lumbberjack, 3D assét.
Figure 14: Qualitative Comparison with multi-view depth estimation model DUSt3R (Wang et al., 2024a). The first row shows that depth estimated with DUSt3R leads to more realistic generation results, while our DA2- based approach also produces consistent and reasonable outputs. The second row presents a failure case of DUS(3R- correction, where our method successfully resolves the hand inconsistency in the baseline. We use the text-based model VideoMV for evaluation, with details consistent with Appendix C.3.
G. Correspondence Visualization of Trajectory Tracking Transformation
In both image-based and text-based generation tasks, we employ the TTM method to track the correspondence of 3D physical points across adjacent views, as illustrated in Fig. 15. TTM without depth guidance (z = 0 in Eq. (4)) exhibits substantial correspondence errors. contrast, the depth-guided TTM method achieves more precise correspondences, thereby enhancing the outcomes of multi-view generation tasks.
H. Discussion with the Point Tracking Method
We incorporate the recent point tracking SOTA method, CoTrrcckr3 (Karaev et al., 2024), as a substitute for TTM for comparison. Tab. shows that while CoTracker3 improves the baseline's 's generation quality to some extent, the gains are less significant than those achieved with TTM. Moreover, Fig. 16 shows CoTracker3 fails to resolve multi-face artifacts, exhibiting noticeable inconsistencies such as multiple legs and misaligned heads, which TTM effectively mitigates. Both quantitative and qualitative results demonstrate that TTM outperforms point tracking methods like CoTracker3 in addressing multi-view inconsistency.
Table 5: Quantitative Comparison with multi-view depth estimation model DUS(3R (Wang et al., 2024a). Our experiments are conducted using single NVIDIA L40S GPU, with VideoMV (Zuo et al., 2024) serving as the baseline. There is trade-off between memory usage and performance between the two methods.
Ablation Study
Time +
Memory Usage + PSNR 1
SSIM t LPIPS
ClipS
Baseline
34 s 107 S (x3.15)
25.00 GB
21.67
0.814
0.206
26.96
DUS(3R
27.04 GB (+2.04)
25.09
0.865
0.109
30.82
DA2 (ours)
98 (x2.89)
25.08 GB (+0.08)
24.82
0.898
0.115
32.78
20

---

MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training- Rectification
Depth Guidance
w. Depth Guidance
w/ Depth Guidance
Depth Guidance
w/o Depth Guidance
Depth Guidance
bear dressed in medieval arwDD,, 3d asset.
A oute steampunke elephant, 3d asse.
w/o Depth Guidance
Depth Guidance
A pág wearing baokpack, asset.
An old vintage ORY, 3d asset.
Figure 15: Correspondence visualization of TTM both image-based and text-based generation. The upper rows illustrate
TTM without depth guidance, whereas the lower
represent the depth-guided TTM. The depth- approach more precisely identifies the correspondence of spatial points between adjacent views. For greater accuracy, readers are encouraged to zoom in on the picture. The areas with the Janus Problem or content drifting are marked with red circles.
21

---

MIRROR: Make Your Object-Levcl Multi-View Generation More Consistent with Training-Fre Rectification
Table 6: Quantitative Comparison of TTM and the point tracking method, CoTracker3 (Karaev et al., 2024). Notably, while CoTracker3 provides slight improvements over the baseline across all metrics, our method achieves more significant gains.
Ablation Study PSNR
SSIM t LPIPS
ClipS
Baseline
21.67
0.814
0.206
26.96
CoTracker3
23.01
0.871
0.124
28.35
TTM (ours)
24.82
0.898
0.115
32.78
Input
Baseline
Point Tracking (CoTraker3)
Ours
An elephant wearing a red soarf SD asset.
A ASLR photo of a bear dressed in medieval armor, Asset.
Figure 16: Qualitative Comparison TTM and the point tracking method, CoTracker3 (Karaev et al., 2024). We use the text-based model VideoMV (Zuo et al.. 2024) for evaluation, with details consistent with Appendix C.3. The second column, based CrTracker3, exhibits noticeable inconsistencies such multiple legs and misaligned heads. In contrast, method effectively corrects these issues.
I. Limitations and Future Work
To ensure uniform multi-view coverage, all baseline models in our paper adopt fixed elevation setting, and MIRROR similarly designed under this constraint. While this facilitates consistent geometric modeling and simplifies the corre- spondence estimation across views, it inevitably limits the applicability of the method in scenarios requiring images from diverse or freely varying viewpoints. This constraint becomes more pronounced in real-world applications where arbitrary camera poses are often necessary. To address this, future work will explore integrating more powerful priors from recent advances in free-viewpoint multi-view diffusion models, thereby extending our trajectory tracking approach to support flexible viewpoint transformations and enhancing the generality of the method.
In addition, our current task setting focuses exclusively on object-level generation. Although this scope allows for fine-grained consistency and controllability, it leaves out broader class of problems centered around scene-level 3D generation-au increasingly important direction in the field. Nevertheless, the core principles of MIRROR remain relevant. We believe our approach provides valuable insights that can help bridge the gap toward scalable scene-level generation. To enable MIRROR to handle such tasks, future work could incorporate layout-aware priors or explicit scene maps to better account for larger spatial contexts and inter-object relationships.
Ultimately, we view MIRROR as a foundational step toward training- free rectification methods for more complex tasks such as the generation of real-world environments, dynamic scenes, and deformable objects, where geometric consistency and view correspondence remain essential yet challenging components.
J. More Ablation Studies
We conducted quantitative ablation studies on stages and dual anchors for the text-based generation task (VideoMV) using 50 prompts from Bench, comparing our method with alternative approaches, as shown in Tab. and Tab. 8. In Tab. 7, the first three rows correspond to ablation experiments on the two-stage strategy, while the last row shows the metries for
22

---

MIRROR: Make Your Object-Level Multi- Generation More Consistent with Training-Fre Rectification
the results generated using our two-stage rectification. And in Tab. 8, the first three rows are ablation experiments on the dual-anchor points, and the last shows the metrics of our dual anchors design. The results of both tables demonstrate that our method outperforms others across multiple metrics.
Table 7: Ablation Study Results on Stages.
Table 8: Ablation Study Results on Dual Anchors.
Ablation Study PSNR + SSIM f LPIPS J ClipS f
Ablation Study
PSNR SSIM f LPIPS ClipS
Stage 1 w/o Rec
21.67
0.814
0.206
26.96
No Anchors
24.56
0.782
0.165
27.36
Stage 1 W. Rec
12.03
0.668
0.469
23.35
Only Positive Anchors
23.27
0.877
0.140
28.15
Stage 2 w/o Rec
23.90
0.884
0.117
24.77
Only Negative Anchors
24.13
0.854
0.122
28.69
Stage w. Rec
Dual Anchors
24.82
0.898
0.115
32.78
24.82
0.898
0.115
32.78
(ours)
(ours)
K. More Results
We present additional results demonstrating the application of MIRROR to rectify baseline models. SyncDreamer (Liu et al., 2023b) and MVD-Fusion (Hu et al., 2024) can generate images from 16 distinct perspectives simultaneously, whereas VideoMV (Zuo et al., 2024) is capable of producing 24 views in a single session. The reduced angle between adjacent frames of VideoMV facilitates a more precise identification of view correspondences, thereby enhancing the accuracy of the refinements. For image-based and text-based generation tasks, please refer to Fig. 17-19 and Fig. 20, respectively. Fig. 17 illustrates the generated results with Syeamre as the baseline. In the first two examples, the baseline model exhibits significant issues with content drift. And the subsequent two examples display problematic geometric shapes. Fig. 18 displays the outcomes generated with MVD-Fusine the baseline. In Fig. 19, the first example exhibits the color drift issue on the 'train' of the baseline model. In the 'teapot' and 'lunch bag". the results generated by the baseline model show inconsistencies across multiple views in terms of shape. And in the last example, it exhibits issues with multiple faces. Fig. 20, which utilizes VideoMV as the baseline model, presents generated results based on text prompts that similarly struggle with a pronounced Janus Problem and low fidelity. After refinement with MIRROR, the generated results significantly surpass all the baseline models. These figures illustrate that our method markedly improves multi-view consistency through incremental refinements during the sampling process, resulting in enhanced quality and superior accuracy.
the baseline. All cases suffer from severe multi-view inconsistency in
L. Failure Cases
Some failure cases are presented in Fig. 21. When the baseline model fails to generate a reasonable geometric structure, is severely flawed, or is completely inconsistent with the prompt, our method struggles to correct such fundamental errors, as the expert priors we rely on are inaccurate. However, by changing the seed to generate well-formed geometric structures from the baseline, this limitation be mitigated the greatest extent.
23

---

MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training- Rectification
SyncDreamer
+MIRROR (ours)
SyncDreamer
+MIRORR (ours)
SyncDreamer
+MIRROR
(ours)
SyncDreamer
+MIRROR (ours)
Figure 17: Generated multi-view images by applying MIRROR on Srnaere for image- diffusion rectification.
The areas with the Janus Problem or content drifting are marked with red circles.
24

---

MIRROR: Make Your Object-Level Multi- View Generation More Consistent with Training-Free Rectification
MVD-Fusion
+MIRROR (ours)
MVD-Fusion
+MIRROR (ours)
MVD-Fusion
+MIRROR
(ours)
MVD-Fusion
+MIRROR
(ours)
Figure 18: Generated multi-view images by applying MIRROR on MVD-Fusion for image-based diffusion rectification. The areas with the Janus Problem or content drifting are marked with red circles.
25

---

MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training- Rectification
VideoMV
+MIRROR (ours)
VideoMV
+MIRROR (ours)
VideoMV
+MIRROR
(ours)
VideoMV
+MIRROR
(ours)
Figure 19: Generated multi-view images by applying MIRROR on VideoMV for image-based diffusion rectification. The areas with the Janus Problem or content drifting are marked with red circles.
26

---

MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training- Rectification
VideoMV
+MRROR (ours)
A oute steawpunnke elepin Ant, 3D asset.
VideoMV
+MIRROR (ours)
A small porcelatn white rabbit figurine, 3D asset.
VideoMV
+MRROR (orrs)
ghost eating or aranges. 3D asset.
VideoMV
+MIROOR (urr
A horse walking, st asset.
Figure 20: Generated multi-view images by applying MIRROR on VideoMV for text-based diffusion rectification.
27

---

MIRROR: Make Your Object-Level Multi-Viey Generation More Consistent with Training- FFee Rectification
Input
Baseline
+MIRROR (Ours)
WORICCS
JU LEW
A white wedding dress on a hanger 3D asset.
Figure 21: Failure Cases for both image-based (SyncDreamer) and text-based (VideoMV) models.
28


Reviews:
[{"review_id": "3pJpx1Tkdn", "reviewer": "Reviewer_3noA", "summary": "This work introduces MIRROR, a training-free plug-and-play module designed to enhance the multi-view consistency of existing text-to-3D and image-to-3D diffusion models. In particular, MIRROR consists of two stages: the first stage leverages an off-the-shelf diffusion model to generate multi-view images, while the second stage obtains the corresponding noise via DDIM and applies feature rectification based on a trajectory tracking module to enhance multi-view consistency during the denoising process. The problem is formally defined and proved, and experiments conducted on several diffusion models verify the effectiveness of the proposed module.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful feedback. We truly appreciate your acknowledgment of our motivation, methods, theoretical proof, and experimental results. Your encouraging comments are highly valued, and we are grateful for your insights!\n\n***Q1. Comparison with Cross-view Attention***\n\nA1. Cross-view attention layers inherently struggle to enforce 3D consistency, as they lack explicit geometric constraints and rely solely on implicit learning through network weights. Given the limited availability of 3D data, such approaches are prone to local optima and often fail to generalize. Existing methods (e.g., MVDream, Consistent-1-to-3, Era3D) still suffer from issues like multi-face artifacts and content drifting at inference stage. Moreover, integrating our method into cross-view attention is impractical, as depth supervision is difficult to extract during training, and the attention layers demand substantial training costs in both computation and time.\n\nIn contrast, FRM offers a more direct and effective solution. Leveraging the progressive denoising process of diffusion models, we introduce explicit multi-view consistency constraints by injecting expert priors from baseline models. It operates without requiring additional 3D supervision and is fully decoupled from the diffusion framework, making it lightweight, plug-and-play, and easily adaptable, which is a more novel approach at a high level. This explicit, geometry-aware mechanism enables FRM to correct inconsistencies more efficiently and reliably than cross-view attention, leading to more stable and coherent multi-view generation results.\n\n***Q2. Handling of Non-Lambertian Surfaces***\n\nA2. This is a promising direction, and it could be addressed by decomposing the generation of non-Lambertian surfaces into two tasks. \n\nFirst, our pipeline enhances multi-view consistency to reconstruct high-quality 3D models, improving the geometric details of objects, which forms a solid foundation for subsequent lighting and rendering.\n\nBuilding on this, lighting models and physics-based rendering techniques can be applied to illuminate and render the 3D model, accurately capturing the reflective properties of shiny surfaces from various viewpoints.\n\nIn other words, while MIRROR enhances the performance of the first task, it also supports the rendering of shiny surfaces. Together, the two processes improve the generation of non-Lambertian surfaces.\n\n***Q3. Scale Consistency of Depth***\n\nA3. Although the depth information comes from the monocular estimation model, the depth ratio across different views remains consistent because the baseline model fixes the camera distance during training. Thus, the monocular model’s estimation capability is sufficient. Given that the depth scale perceived by the baseline model is fixed, we can use it as a scale factor to convert normalized relative depth into absolute depth, ensuring depth alignment across views.\n\nFurthermore, we perform a grid search for scale factors across multiple viewpoints and apply dual-anchor-based fusion of block features in FRM to mitigate potential scale shifts in TTM. We believe this approach is in line with your suggestion, and we will add these discussions in the appendix for further clarification.\n\nWe hope our response has effectively addressed your concerns. Your suggestions have offered valuable insights that will greatly inform our future work, and we deeply appreciate them. Thank you once again for your constructive comments.", "claims_and_evidence": "Kindly refer to **Other Strengths And Weaknesses**", "methods_and_evaluation_criteria": "Kindly refer to **Other Strengths And Weaknesses**", "theoretical_claims": "Kindly refer to **Other Strengths And Weaknesses**", "experimental_designs_or_analyses": "Kindly refer to **Other Strengths And Weaknesses**", "relation_to_broader_scientific_literature": "Kindly refer to **Other Strengths And Weaknesses**", "essential_references_not_discussed": "Kindly refer to **Other Strengths And Weaknesses**", "other_strengths_and_weaknesses": "## Strengths:\n\n* The idea of enforcing consistency among adjacent frames to improve multi-view consistency is interesting and well-motivated.\n* The problem is formally defined and well-proven.\n* Experiments are conducted on several baseline models to demonstrate the versatility of the proposed module.\n\n## Weaknesses:\n\n* Unclear justification for multi-view diffusion limitations. It is unclear why multi-view diffusion alone cannot guarantee multi-view consistency, while the proposed regularization among adjacent frames can. Multi-view diffusion applies cross-view attention to enforce correspondence among generated views, which at a high level seems similar to the motivation behind the MIRROR module. A more detailed discussion and analysis of how FRM is more effective than cross-view attention in addressing multi-view consistency would be beneficial.\n\n* Handling of non-Lambertian surfaces. Non-Lambertian (shiny) surfaces exhibit view-dependent effects, which contradict the underlying assumptions of MIRROR. It would be interesting to see how MIRROR will perform on assets with shiny surfaces.\n\n* Impact of monocular depth inconsistency on TTM. Since the depth information is extracted from a monocular model, it cannot guarantee consistent scale and shift across different views. It would be interesting to know whether this issue affects overall performance. If so, would metric-depth or normalized scale-invariant depth be helpful in mitigating this problem?", "other_comments_or_suggestions": "N.A.", "questions_for_authors": "N.A.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NKwHjpPk2J", "reviewer": "Reviewer_BJCB", "summary": "The author present MIRROR, an efficient, training-free, plug-and-play method to enhance multi-view consistency in 3D asset generation. The proposed approach directly rectifies latent noisy images across views during the sampling process. To be specific, a Trajectory Tracking Module based on depth information is proposed to ascertain corresponding positions of points across distinct views. It use a Feature Rectification Module to eliminate ambiguity by enforcing consistency in the representation of the same physical point across different viewpoints. Qualitative and quantitative experiments demonstrate that MIRROR consistently enhances the performance of various generators.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your valuable suggestions and the recognition of our work in methodology, theoretical proof, and experimental design. Below are our responses to your concerns, and we hope they help clarify any doubts you may have.\n\n***Q1. More Metrics*** \n\nA1. On the one hand, for fairness, we adopt the same quantitative metrics as baselines (including PSNR, SSIM, LPIPS, and CLIP Score), which are also widely used in current practice of multi-view generation. The quantitative results align well with the qualitative observations, further supporting the reliability of these metrics. In addition, **Reviewer y23b** acknowledges the quantitative metrics we used in the fourth line of their review in the **\"Claims and Evidence\"**.\n\nOn the other hand, we additionally employ Chamfer Distance and Volume IoU to evaluate the 3D consistency of the reconstructed geometry from the generated multi-view images as follows:\n\n\n| Models | Chamfer Distance↓ | Volume IoU↑ |\n| -------------------- | ----------------- | ----------- |\n| SyncDreamer          | 0.0415            | 0.5137      |\n| **+MIRROR (ours)**   | **0.0387**        | **0.5296**  |\n| VideoMV (text-based) | 0.0459            | 0.5381      |\n| **+MIRROR (ours)**   | **0.0276**        | **0.6264**  |\n\nFor text-based methods, following VideoMV, we sample half of the views at regular intervals to reconstruct a pseudo-ground truth mesh for evaluation. Other evaluation settings follow those in Appendix C.4. The 3D reconstruction metrics in the table further validate the effectiveness of our method in improving the quality and consistency of both multi-view images and 3D reconstruction.\n\n***Q2. Depth Estimation***\n\nA2. All versions of Depth-Anything-V2 (DV2) achieve over 95% accuracy, with no significant performance differences observed in our task. Considering both accuracy and model size, we adopt DV2-Small as the depth estimation module.\n\nMoreover, based on the fixed camera distance used during training of each baseline, we convert the relative depth to metric depth using a consistent scale factor. Specifically, the baseline model, trained and inferred under circular camera poses, inherently provides a fixed depth scale, which ensures cross-view consistency. This allows the relative depth predicted by DV2 to be reliably transformed into absolute depth, ensuring consistent metric depth across multiple views.\n\nWe hope our response could address your concerns. Based on your suggestions, we will include additional metrics in the appendix. Thank you once again for your recognition of our work and providing such insightful recommendations!", "claims_and_evidence": "The major claim of the proposed approach to be efficient, training-free, plug-and-play and enhance multi-view consistency is well supported by experimental results.", "methods_and_evaluation_criteria": "Utilizing point-to-point corrections to ensure point features' consistency makes sense. Using the similarity computed between the feature of each pixel to provide a gradient map is an elegant way to guide the denoising procedure.", "theoretical_claims": "I check the proofs for the two major module and they look correct to me.", "experimental_designs_or_analyses": "1. The author use multiple current multi-view generation approaches as baselines and compare the generation results with and without enhancement using the proposed approach. The experiment results showing that incorporating MIRROR into these baselines resolves the prominent artifacts and multi-face issues encountered in baselines.\n\n2. Comprehensive experimental analysis and ablation studies are give to demonstrate proposed approach's effectiveness and help understand how it works.\n\n3.  The authors use PSNR, SSIM, LPIPS, and Clip Score as metrics for consistency. I have a concern on whether they can serve as effective metrics.", "relation_to_broader_scientific_literature": "This approach can serve as a plug-and-play module to the literature of multi-view generation, which may make impacts.", "essential_references_not_discussed": "Essential references are well-discussed.", "other_strengths_and_weaknesses": "The implementation and experiments details are well-described.", "other_comments_or_suggestions": "n/a", "questions_for_authors": "1. What version of Depth-Anything-V2 is used? Does it output metric depth or relative depth? Any discussions on the depth scale consistency among multiple views?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Q4KGL49l3y", "reviewer": "Reviewer_y23b", "summary": "The paper introduces MIRROR, a training-free, plug-and-play method that improves consistency in multi-view image generation using diffusion models. At its core, MIRROR uses two novel modules: the Trajectory Tracking Module (TTM), which pinpoints corresponding 3D points across views using depth maps, and the Feature Rectification Module (FRM), which aligns features during sampling to fix inconsistencies. Unlike methods that require fine-tuning, MIRROR works directly during inference, making it compatible with popular pre-trained models like SyncDreamer and VideoMV. Experiments show it effectively tackles the Janus problem and content drift while preserving photorealism, offering a lightweight solution for high-quality 3D generation.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your thorough and detailed review, offering valuable insights on methodology, theory, experiments, and scalability, and thank you for recognizing our work!\n\n***Q1. Limitation of TTM***\n\nA1. (1) TTM is designed to ensuring uniform geometry coverage and is theoretically extendable to any pose. In fact, sampling multiple views around a fixed elevation outperforms arbitrary 6DoF poses for reconstruction by reducing occlusions and preserving overall geometry. Specifying a set of elevations and sampling around each can further enhance results.\n\n(2) The number of views is determined by baselines, providing flexibility and decoupling from MIRROR.\n\n(3) Optical flow is unsuitable for multi-view generation due to high computational overhead, while dedicated point trackers suffer from drift during viewpoint rotation, causing geometric and texture distortions.\n\nIn contrast, TTM enables fast tracking with block-level spatial fusion, reducing errors. 3D metrics in **Reviewer BJCB, A1**, also confirm our effectiveness.\n\n***Q2. Broader Applicability***\n\nA2. Our task focuses on object-level multi-view generation and reconstruction, a key branch of 3D generation, with baselines being powerful mainstream methods. Generation of real-world or dynamic scenes and deformable objects are other important branches, and we believe MIRROR could inspire advancements in these areas. More details refer to **Reviewer ir3S, A3**.\n\n***Q3. Definition of Eq.(5)***\n\nA3.  Eq.(5) defines the tracking error upper bound as base model's sampling error, with $x_0$ and $\\hat{x}_0(x_t, t)$ representing the true image and predicted image at state t, respectively.\n\n***Q4. Proof of Eq.(15)***\n\nA4. Using the first-order Taylor expansion, we obtain:\n$$\nH(x_0)=H(\\hat{x}_0(x_t,t))+\\nabla H(x_0) (x_0-\\hat{x}_0(x_t,t))+o(||x_0-\\hat{x}_0(x_t,t)||).\n$$\nHere, $H$ is a pretrained ViT network with a continuous, bounded gradient that outputs scale-consistent absolute depth, showing that the depth-level error is of the same order as the pixel-level error:\n$$\n||H(\\hat{x}_0(x_t,t))-H(x_0)||\\approx||\\nabla H(x_0)(x_0-\\hat{x}_0(x_t, t))||\\simeq O(||x_0-\\hat{x}_0(x_t,t)||).\n$$\n***Q5. Derivation of Eq.(19)***\n\nA5. There was a typo in Eq.(18), now corrected as:\n$$\n\\nabla_{z_t}\\log p_\\theta(z_t)=-\\frac{1}{\\sqrt{1-\\overline{\\alpha}_t}}\\varepsilon _ \\theta(z_t,t).\\tag{18}\n$$\nUsing Eq.(17) and (18), Eq.(19) is easily derived:\n\n$$\n\\varepsilon_\\theta(z_t,t,c)=\\varepsilon_\\theta(z_t,t)-\\sqrt{1-\\overline{\\alpha}_{t}}\\nabla _ {z_t} \\log p _ \\theta(c|z_t).\\tag{19}\n$$\n***Q6. Depth Estimation***\n\nA6. Fig.12 and Tab.3 shows improving depth estimation accuracy benefits MIRROR, but this does not necessarily mean that multi-view depth estimation methods are superior.\n\nOur goal is a lightweight plugin to enhance consistency. While methods like Dust3R and Mast3R improve robustness, they require constructing multi-view cost volumes or 3D reconstruction, leading to high memory usage (2GB vs. 100MB for DA2) and slow inference, which compromises our advantages. Besides, in monocular tasks, DA2 significantly outperforms Dust3R, indicating that multi-view methods, despite improving depth consistency, may exacerbate Janus Problem due to the accumulation of estimation errors during denoising.\n\nFor depth scale consistency, refer to **Reviewer ir3S, A1**.\n\n***Q7.  Selection of s(t)***\n\nA7. s(t) aims to match the consistency gradient magnitude with $\\varepsilon_\\theta$. With negligible differences across models, we use a general parameter, though adjustments per model are feasible.\n\n***Q8.  Failure Cases and Limitation of Baselines***\n\nA8. Failure cases (see Fig.2 in https://anonymous.4open.science/r/mirror-A9B9/figs.pdf) show that when the baseline produces unreasonable, severely flawed geometry (an inherent limitation), we struggle to correct these fundamental issues.\n\n***Q9.  Essential References***\n\nA9. Without training, ConsiStory enhances subject consistency in text-to-image generation by modifying the network with Subject-Driven Shared Attention and Feature Injection. However, it lacks plug-and-play compatibility with other models, limiting generalizability.\n\nMulti-view diffusion models generate consistent images from noise, while inverse diffusion infers the initial state from known outcomes, with distinct objectives.\n\n***Q10.  DreamFusion***\n\nA10. You're right. We'll clarify it.\n\n***Q11.  Text-based Results***\n\nA11. $x_0$ is the theoretical true image in tracking loss (5). As the text-based method lacks a reference image, VideoMV uses half of the views for reconstruction and rendering as pseudo-ground truth. Moreover, text-based tasks are more challenging and diverse than image-based ones, so small corrections in the denoising process have a stronger impact, highlighting MIRROR's power through both qualitative and quantitative improvements.\n\nHope our responses address your concerns. A detailed revision will be in the appendix. Thanks again for your comprehensive feedback.", "claims_and_evidence": "- MIRROR improves multi-view consistency in diffusion-generated images.\n\nQualitative: Visual comparisons (Fig. 1, 5) show MIRROR resolves artifacts like the Janus problem (e.g., multiple faces) and content drift (e.g., misaligned geometry) in baselines (SyncDreamer, VideoMV).\n\nQuantitative: Metrics (Table 1) confirm gains in PSNR (up to +3.15), SSIM (up to +0.084), and LPIPS (up to -0.091), indicating improved alignment and reduced perceptual inconsistency.\n\n- MIRROR is a training-free, plug-and-play solution compatible with existing models.\n\nUses DDIM inversion and rectification during inference (Fig. 3), requiring no fine-tuning or architectural changes to baselines.\n\nApplied successfully to diverse models (SyncDreamer, MVD-Fusion, VideoMV) for both image- and text-based tasks (Table 1, Fig. 5).\n\n- Depth-guided trajectory tracking enables precise geometric alignment.\n\nRemoving depth guidance (Fig. 7) leads to erroneous correspondences (e.g., mismatched limbs on animals, distorted shapes).\n\nProposition 4.2 and Appendix D show tracking errors diminish as denoising progresses, ensuring stable rectification.\n\n- Feature rectification achieves efficiency without sacrificing quality.\n\nOmitting UNet Jacobian terms (Theorem 4.4) reduces inference time by ~50% (Table 2) with negligible performance loss.\n\nDual-anchor fusion (Fig. 8) filters background noise while retaining critical features, validated by improved SSIM/LPIPS (Table 5).", "methods_and_evaluation_criteria": "- Methods\n\nThe proposed method employs a two-stage rectification pipeline:\n\n1. Utilizes off-the-shelf models (e.g., SyncDreamer, VideoMV) to synthesize initial multi-view images.\n\n2. Rectification via TTM and FRM:\n\n    Trajectory Tracking Module (TTM): Uses monocular depth estimation (Depth-Anything-V2) to establish 3D correspondences across views.\n\n    Feature Rectification Module (FRM): Aligns pixel embeddings via dual-anchor fusion and gradient guidance, applied during DDIM sampling.\n\n**Limitations:**\n\nTTM assumes fixed elevation angles (Eq. 4), limiting applicability to rigid objects and predefined camera paths (e.g., azimuth-only rotations).\n\nEvaluated only on object-centric models (image/text-to-multi-view). Applicability to multi-view conditioned (e.g., EscherNet, CAT3D) or scene-level methods (e.g., CameraCtrl, MotionCtrl) remains unverified.\n\n\n- Evaluation\n\nDatasets: GSO (image-based), T3Bench (text-based).\n\nMetrics: PSNR, SSIM, LPIPS (multi-view consistency), CLIP score (text alignment).\n\nQuantitative and qualitative results clearly demonstrate the improvement over initial baseline models.", "theoretical_claims": "The paper presents several theoretical justifications, particularly in:\n\n- Trajectory tracking convergence analysis (Proposition 4.2).\n\nThe proof tries to show that tracking error is bounded by diffusion model errors.\n\n- Gradient-based rectification (Theorem 4.4).\n\nThe derivation in Appendix B.3 shows that neglecting the UNet Jacobian term introduces bounded errors, allowing efficient rectification by skipping the diffusion model backpropagation.\n\n**Concerns**\n\nWhile the theoretical framework is conceptually sound, gaps in notation, unverified assumptions, and incomplete derivations weaken rigor. Addressing these would strengthen the theoretical foundation.\n\nEquation (5) uses, which is not clearly defined in the main paper.\n\nEquation (15) in Appendix B.1 suggests that depth error is always smaller than pixel error, but this is not rigorously proven.\n\nEquation (19) contains typos, missing the conditional term . Also, the transition from Eq. (17) and (18) to (19) is not obvious, particularly in obtaining the coefficients. A more detailed derivation would improve clarity.", "experimental_designs_or_analyses": "The comparison with baselines is thorough, covering multiple models and metrics.\n\nThe ablation studies (Fig. 6, 7, 8) effectively isolate contributions from TTM, FRM, and dual-anchor fusion.\n\nThe timing analysis (Table 2) provides strong evidence that MIRROR is computationally efficient.\n\n**Concerns**\n\nThe proposed method only works with camera poses at the same elevation angle (Eq. 4, Section 4.2), which is quite limited. How about using more generic point tracking methods, such as optical flow or dedicated point trackers?\n\nWill the current setting work with models that support arbitrary 6DoF poses and flexible numbers of views, such as EscherNet and CAT3D?\n\nDepth estimation is done using a monocular method. Could recent multi-view depth estimation approaches (e.g., Dust3R, Mast3R) improve scale and 3D consistency?", "relation_to_broader_scientific_literature": "MIRROR builds on multi-view diffusion models (e.g., SyncDreamer, VideoMV) and depth-guided correspondence tracking. It differentiates from epipolar geometry-based methods (Ye et al., 2024; Zhou & Tulsiani, 2023). It also relates to inverse diffusion problems and test time optimization methods.", "essential_references_not_discussed": "**Limitations**\n\nThe paper does not discuss concurrent training-free rectification techniques and inverse diffusion papers, such as ”Denoising Diffusion Restoration Models“，“SOLVING VIDEO INVERSE PROBLEMS USING IMAGE DIFFUSION MODELS”\n\nThe description of DreamFusion is not entirely accurate. While DreamFusion does require optimizing the 3D representation (e.g., NeRF or Gaussian Splatting), it does not require backpropagation through the diffusion network. This should be clarified.\n\nRecent works like Dust3R (2024) or Mast3R (2024) could enhance correspondence tracking and depth estimation but are not discussed.\n\nMethods like EscherNet (2024) or CAT3D (2024), which handle arbitrary 6DoF poses and multiview conditioning, are omitted.", "other_strengths_and_weaknesses": "**Concerns**\n\nReference image selection in the text-based method for tracking loss is unclear. In Fig. 5, applying MIRROR causes significant changes in text-based outputs compared to single-image-based results. Why?\n\nTable 1 shows that text-based methods gain the most improvement. Why is this the case?", "other_comments_or_suggestions": "How does MIRROR perform on real-world multi-view data? The evaluated data is either synthetic or under perfect lighting/imaging conditions.\n\nHow sensitive is MIRROR to depth estimation errors, and could improving depth estimation further enhance performance?\n\nWhy is the trajectory tracking method restricted to the same elevation? Could more flexible tracking methods be incorporated?\n\nHow does MIRROR handle dynamic scenes or deformable objects where depth varies non-rigidly across views, say there is pretrained model can do 4D NVS.", "questions_for_authors": "Please see my questions above and all the **limitations** and **concerns** in each part.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZEMTfWRJWv", "reviewer": "Reviewer_ir3S", "summary": "This paper introduced  MIRROR, as a training-free rectification to improve the consistency of multi-view generation. The main contributions can be divided into (1) Trajectory Tracking Module (TTM) to pixel-wise trajectory tracking that labels identical points across views and (2) Feature Rectification Module (FRM) for explicit adjustment of each pixel embedding on noisy synthesized images by minimizing the feature distance. The overall idea is interesting, but the presentation of this paper is very unclear, while some details should be further clarified.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough analysis and constructive feedback on our paper. We will address the concerns you raised and hope our responses will clarify your doubts.\n\n***Q1.  Scale of Depth***\n\nA1. Based on the camera parameters of the base model, we approximate relative-to-metric depth conversion and achieve depth alignment across multiple views. \n\nAs the object-camera distance is fixed during training, the model learns a consistent depth scale under circular camera poses. This scale factor allows transforming relative depth into absolute depth for cross-view alignment. We further apply grid search over scale factors and use dual-anchor fusion of block features to reduce potential scale shifts during inference. The transformed depth enables more accurate trajectory tracking in TTM via geometric warping.\n\nDetails on Eq. (5) can be found in **Reviewer y23b, A3**. And alignment details of depth estimation will be included in the appendix of the revised version.\n\n***Q2.  More Quantitative Results***\n\nA2. Based on your suggestion, we have provided additional quantitative evaluation results. Please refer to the table in **Reviewer BJCB, A1**, for details.\n\n***Q3.  Extension to Scene-level Tasks***\n\nA3. Importantly, the nature and manifestations of inconsistency differ between object-level and scene-level multi-view generation tasks. Object-level inconsistency mainly arises from the lack of 3D structural modeling and consistency supervision, often manifesting as the Janus problem and content drifting. In contrast, scene-level tasks typically suffer from layout disarray and semantic drift due to the absence of structural representations and layout supervision. Thus, the underlying challenges are fundamentally different.\n\nOur task track focuses on object-level multi-view generation, a key branch in 3D generation, with the baseline models representing mainstream, state-of-the-art methods. Our motivation is to correct the inconsistencies in object-level base models via explicit consistency supervision in a lightweight, plug-and-play, training-free manner, thereby improving 3D reconstruction quality.\n\nWhile scene-level generation is another important branch with fundamentally different inconsistency issues, we believe our method can offer insights for advancing this direction. To adapt MIRROR to this task, future work could incorporate layout-aware priors or scene maps to handle the broader spatial context. We consider MIRROR a core foundational step toward such extensions, and potential directions for scene-level adaptation will be discussed in the appendix.\n\n***Q4.  Symbols in FRM***\n\nA4. We corrected the typo by redefining the 3×3 block as $B_{u _ \\alpha}$ instead of $M(u _ \\alpha)$ to align with the notation used in subsequent sections. Following your suggestion, we clarify key symbols in FRM, along with the updated pipeline  (see Fig. 1 in https://anonymous.4open.science/r/mirror-A9B9/figs.pdf).\n\n$u, u_\\alpha$ denote the coordinates of a point in the current view and its corresponding tracked point in the neighboring view, respectively. And $Z(u), Z_\\alpha(u_\\alpha)$ represent the feature values indexed by point $u$ and $u_\\alpha$. By traversing all features ${Z_\\alpha(v), v \\in B_{u_\\alpha}}$, within block $B_{u_\\alpha}$, and applying the dual-anchor weights $W$, we obtain the aggregated block feature $\\mathcal{M}(Z_\\alpha(u_\\alpha))$. The L2 feature distance between $Z(u)$ and $\\mathcal{M}(Z_\\alpha(u_\\alpha))$ is then computed by Eq. (10) to form the consistency correction loss.\n\n***Q5. Background Exclusion***\n\nA5. As mentioned in our response A1, irrelevant background information would not caused by depth warping. The design of negative anchors aims to exclude both the additional information from depth map downsampling into the latent space and the redundant or irrelevant signals arising from expanding point features into block-level form to preserve spatial continuity.\n\nThis design does not hinder extension to scene-level tasks. The dual-anchor feature fusion mechanism effectively suppresses irrelevant information while enhancing the contribution of relevant features. Moreover, dual anchors and their weights can be adapted to different application scenarios for better generalization.\n\nWe will incorporate the revisions into the paper and hope our responses could address your concerns. \n\nWe sincerely believe that our work is deserving of acceptance, and we would be grateful if you could recognize the contributions we’ve made. We kindly hope that, you might consider raising the score accordingly. Thank you again for your thoughtful feedback and consideration!", "claims_and_evidence": "No. After reading the paper, I still could not understand why Trajectory Tracking Module (TTM) works with monocular depth estimation (depthanythingV2) without any metric alignment. The monocular depth is scale-invariable. Unlike the metric depth, monocular depth fails to be directly used as the condition of geometric warping.", "methods_and_evaluation_criteria": "Yes. This paper proposed both qualitative and quantitative results based on various base methods, showing the effectiveness.", "theoretical_claims": "This paper includes some theoretical claims. However, the claim of Eq.5 is questionable, i.e., $t\\rightarrow 0$ leads to the convergence of the trajectory tracking operator, because the depth is not aligned as the metric depth. Even the depth is extracted from $x_0$, it still fails to be used as the warping condition.", "experimental_designs_or_analyses": "Yes. Most results are based on qualitative comparisons.", "relation_to_broader_scientific_literature": "The proposed method is a general approach to improve the consistency of multi-view diffusion models. But the discussion of this paper is limited to the object level with simple backgrounds, failing to be extended to the scene level.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Except for the issue of TTM mentioned above. The presentation of this paper is also unclear, especially Sec4.3 is very hard to follow.\nMany symbols are defined, but their usage is not clearly discussed. For example, Line231(right) defines the 3x3 block as $M(u)$, which has completely not been used and mentioned in the subsequent paragraphs at all. Moreover, what are the meanings of  $Z_{\\alpha}$, $v\\in B_u$. How to understand the feature distance of FRM (which symbol indicates block feature)?", "other_comments_or_suggestions": "N/A", "questions_for_authors": "The authors should clarify about the usage of monocular depth in TTM, and clear and detailed presentation about Sec.4.3. It would be beneficial if the authors could present a clearer overview pipeline, while most symbols are labeled in this pipeline.\n\nBesides, there is another question: why the invalid background information should be explicitly excluded during the feature information fusion, even if the TTM is correct? Is this problem caused by the incorrect depth warping, resulting in mistakenly relating to background regions? Would this limit the extension to scene-level multi-view generation?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The submission received 1 accept, 2 weak accepts and 1 weak reject. The reviewers generally found the proposed methodology to be well-motivated, interesting and designed elegantly. They also found the experiments to be comprehensive, with results validating the claims in the paper. One main concern was that the limitation to object-level multi-view generation was not adequately communicated, but in the rebuttal the authors have volunteered to define the narrower task more clearly in the title, abstract and introduction. There were other concerns about proofs of certain equations, comparison to other methods, and use of other metrics — these were effectively addressed by the authors in the rebuttal. 

The AC concurs with the majority of the reviewers in recommending to accept this paper. The authors are requested to incorporate new and updated material from their rebuttals into the submission, as well as the agreed changes to more clearly convey the object-centric scope limitation of their work.
Decision: Accept (poster)