MVCustom: Multi-View Customized Diffusion via Geometric Latent Rendering and Completion
Authors: Minjung Shin
Hyunin Cho
Sooyeon Go
Jin-Hwa Kim
Youngjung Uh
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-view customization
Multi-view generation
Customizaton
Personalization
Diffusion models
Primary area: generative models

Abstract: Multi-view generation with camera pose control and prompt-based customization are both essential elements for achieving controllable generative models.  
However, existing multi-view generation models do not support customization with geometric consistency, whereas customization models lack explicit viewpoint control, making them challenging to unify.
Motivated by these gaps, we introduce a novel task, multi-view customization, which aims to jointly achieve multi-view camera pose control and customization.
Due to the scarcity of training data in customization, existing multi-view generation models, which inherently rely on large-scale datasets, struggle to generalize to diverse prompts.
To address this, we propose MVCustom, a novel diffusion-based framework explicitly designed to achieve both multi-view consistency and customization fidelity. 
In the training stage, MVCustom learns the subject's identity and geometry using a feature-field representation, incorporating the text-to-video diffusion backbone enhanced with dense spatio-temporal attention, which leverages temporal coherence for multi-view consistency. In the inference stage, we introduce two novel techniques: depth-aware feature rendering explicitly enforces geometric consistency, and consistent-aware latent completion ensures accurate perspective alignment of the customized subject and surrounding backgrounds. 
Extensive experiments demonstrate that MVCustom achieves the most balanced and consistent competitive performance across multi-view consistency, customization fidelity, demonstrating effective solution of multi-objective generation task.

Published as conference paper ICLR 2026
MVCUSTOM: VELTI-VIEW CUSTOMIZED DIFFUSION VIA GEOMETRIC LATENT RENDERING AND COMPLETION
Minjung Shin'
Hyunin Cho
Sooyeon Go
Jin-Hwa Kim SNU AlIS
Youngjung Uh!
Yonsei University
NAVER Al Lab
set of multi-view reference images
Inference conditioned pose and text description
Text "A y teddybear under Christmas free surrounded by presents.
DB LoRA MVgan
Viewo Subject Customiicatio
TxL.VV MV Custom ling
Targel camera puses
Figure 1: Comparison between Custom and existing approaches extended to multi- view customiza- tion. The light blue box shows the reference multi-view images and corresponding camera poses of a customized object. The 'X' marks indicate regions inconsistent with either the reference object's appearance across views, while 'O' marks indicate well- cellmmaint consistency. Our approach clearly outperforms existing methods by achieving accurate viewpoint alignment and robust multi-view consistency for both the customized object and novel surroundings generated from diverse textual prompts.
ABSTRACT
Multi-view generation with camera pose control and prompt-based customization are both essential elements for achieving controllable generative models. However, existing multi- view generation models do not support customization with geometric consistency, whereas customization models lack explicit viewpoint control, making them challenging to unify. Motivated by these gaps, introduce novel task, multi-view customization, which aims to jointly achieve multi-view camera pose control and customization. Due to the scarcity of training data customization, existing multi-view generation models, which inherently rely on large-scale datasets, struggle to generalize to diverse prompts, To address this, we propose MVCustom, a novel diffusion-bartt framework explicitly designed to achieve both multi-view consistency and customization fidelity. In the training stage, MVCustom learns the subject's identity and geometry using feature-field representation, incorporating the text-to-video diffusion backbone enhanced with dense spatio-temporal attention, which leverages temporal coherence for multi-view consistency. In the inference stage, we intro- duce two novel techniques: depth-aware feature rendering explicitly enforces geometric consistency, and consistent-and aware latent completion ensures accurate perspective alignment of the customized subject and surrounding backgrounds. Extensive experiments demon- strate that M VCustom achieves the most balanced and consistent competitive performance across multi-view view consistency, customization fidelity, demonstrating effective solution of multi-objective generation task. Project page:
*Correspodiigg author.

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Published as a conference paper ICLR 2026
Task
Method
Fidelity
Holistic
S.MV
H.MV
(a) Customization
DreamBonth, CustomDiifusionn, etc.
0
X
(b) Subject-only text-to-MV gen.
FlexGen, Make- Your-3D, etc.
(c) Text-to-MV generation
CameraCtri, ViewDifff etc.
0
0
(d) Subject-only imagc-to-MV gen.
SV3D, SyeaDre,mer, etc.
X
X
0
X
(e) Image-to-MV gen.
SEVA, CAT3D, ViewCrafter, etc.
X
0
0
0
(f) Viewpoint-aware subject custom.
CustomDiffusion3600 CustomNet
O
0
0
X
(g) Multi-view customization
MVCustom (ours)
O
Table 1: Comparison of existing tasks and representative methods. Fidelity refers to preserving object identity from reference images and alignment with textual prompts in customization. Holistic denotes whether both subjects and the surroundings described in a prompt are synthesized. S.MV evaluates whether subjects remain consistent across different viewpoints. H.MV consistency refers whether both subjects and their surroundings are holistically consistent across viewpoints. MV stands for multi-view.
1 INTRODUCTION
As generative models advance rapidly, users are increasingly demanding fine- fine-grained controllability. Among the essential elements, two forms of control are significant: camera control and customization. First, camera control is to generate images for specified viewpoints, which is essential in domains such as 3D understanding. In particular, ensuring camera pose control and multi-view consistency for both the subject and its surroundings is crucial for realistic and immersive content, misalignment across views severely undermines geometric coherence. Second, customization is to capture user-specific subjects, or concepts, supporting personalized content generation and supporting applications such as creative media and design prototyping. etc.
While each form of control is valuable on its own, integrating them unlocks significantly richer applications. A unified framework that supports both capabilities enables 3D customization for virtual prototyping and personalized asset generation, where both user-specific fidelity and geometric consistency are indispensable. Moreover, broadens the scope of controllable generative models, enabling realistie, immersive, and user- tailored content beyond the reach of existing approaches. To this end, we introduce the novel task of multi-view customization, which requires (1) generating images that adhere to specified camera parameters for consistent perspective alignment, (2) preserving subject identity provided by reference images, and (3) coherently adapting both subjects and their surrounding context to diverse textual prompts.
However, to the best of our knowledge, prior method fully satisfies the requirements of the multi-view customization. As summarized in Tbl. conventional customization methods (Lee et al., 2024 Ruiz et al.)
2023 Kumari al. 2024) preserve reference identity and align with prompts, but lack viewpoint control.
Most multi-view generation methods focus only subjects, neglecting consistent surroundings across views (cases b, in Tbl. I). Some holistic multi-view generation methods (He al.) 2024 Zhou al. 2025) provide full-frame consistency but do not support personalization to novel reference concepts (cases c. c). Viewpoint- aware subject customization methods (Kumari et al. 2024, Yuan al.) (023) remain subject-centric, leading to inconsistent surroundings across views (case f). These limitations underscore the need for new approach explicitly designed for multi-view customization.
Directly adopting multi-view generation frameworks, which rely heavily on large-scale training data, is infeasible in the customization setting, where only a few reference images are available. straightforward baseline applies conventional customization methods (Ruiz et al. 2023 Hu et al., 202I) directly to text- conditioned multi-view backbones (c in Tb.. D.. but this approach cannot preserve subject identity and reduces camera pose control ability. Another naive baseline generates single customized image, then applies image-conditioned multi-view generation models (f in Tbl. ID. but the inherent ambiguity of a single view leads to inconsistent spatial relationships and degraded fidelity, illustrated in Fig.
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To address these challenges. we propose MVCustom, a diffusion-basse framework explicitly designed for robust multi-view customization. Our method separates training and inference stages to effectively handle limited data and ensure geometric consistency across diverse prompts. In the training stage, we leverage pose-conditioned transformer blocks (Kumari et al.] 2024). However, a key change is using the video diffusion backbone enhanced with dense spatio-temppraa attention to transfer temporal coherence into holistic- frames consistency, ensuring spatial coherence of both the subject and their surroundings across views. At inference, the key challenge is ensuring multi-view geometric consistency for novel prompts, particularly for the subject's surroundings that lack supervision from limited training data. To address this, we introduce two novel inference-stage techniques: depth-aware feature rendering, which explicitly enforces geometric consistency using inferred 3D scene geometry, and consistent-award latent completion, which naturally completes previously unseen regions revealed by viewpoint shifts. Extensive comparisons demonstrate that MVCustom is the only approach that effectively integrates accurate multi-view generation and high-fidelity customization.
Our contributions are summarized as follows:
We propose a novel task, multi-view customization, clearly define its requirements, and systematically analyze the limitations of existing methods and tasks.
We introduce a video diffusion-based backbone enhanced with dense spatio-temporal attention modules, effectively transferring temporal coherence into multi-view consistency.
To accommodate limited data in customization, we propose two novel inference-stage methods: depth- aware feature rendering for explicit geometric consistency, and consistent- -aware latent completion for consistent and realistic completion of disoccluded regions.
2 RELATED WORK
Conventional text-based customization. Customization methods generate images guided by textual prompts while preserving identities from reference images, typically by learning concept-specific embed- dings Gaa et al. 2022). fine-tuning models (Ruiz et al. 2023). or applying lightweight adaptations (Hu et al.
2021). Recent approaches further enhance text-image alignment (Alaluf et al., 2023 et al. 2024a) and
multi-subjcct control (Kumari et al. 2023 Kwon & Ye 2024). However, these methods typically lack explicit
control over viewpoint. Some works achieve pose-varant compositions (Li et al. 2024b Song et al., 2024). but do not support explicit camera pose control. Methods like CustomDiiffusion366 (Kumari et al. 2024)
and CustomNet (Yuan et al. 2023) incorporate viewpoint control yet remain predominantly subject-centric, neglecting coherently represent their surroundings. In contrast, our proposed MVCustom explicitly ensures robust spatial coherence for both customized subjects and surroundings across diverse viewpoints.
Multi-view generation. Multi-view generation models (Zhao et al. 2025 Tang et al. 2024 Alper et al.
2025 Shin al. (023) focus on synthesizing consistent multiple views. However, these models typically require large datasets to learn 3D geometry and inpaint newly visible regions, making them unsuitable for customization with only few reference images. An alternative approach may involve applying conventional customization methods directly onto multi-view generation backbones. Nevertheless, text-conditioned multi-
view generation models (Höllein et al., 2024 Shi et al., 2023 Tang et al.. 2023) Huang et al.] (024)) are limited by the scarcity of paired text and multi-view data, leading to poor adaptability to diverse textual prompts. Another related approach utilizes multi-view diffusion models (Long et al. 2024) for novel-view synthesis from single reference image, enabling subject-aware editing in multi view settings (Liu et al. 2024). However, these methods primarily focus only subject editing. In contrast, our MVCustom framework explicitly addresses these challenges, combining effective 3D geometry learning with explicit inference-time geometric constraints, enabling robust multi-view consistency and precise alignment with diverse textual prompts.
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Main Branch
Sprto-lecppoall Transfomer
Pretrained T2V model
Self-Amemtion
Aiintintttiioon
Projection Tayer
F Frese
Trainable
Preaze
Conditioning Camera Pose
Multsview Branch
Pretrained
20
SA
CA
X,
X, + X
PL
STT
T2V model
19
Main Branch
Overall training pipeline
Reghr
10
Time
SA
CA
FF
NeRR
STT
ID Temporal
Spatio-semporal
(X
Attention
Spurse Dense
Attention
(b) Spatio-temporal attension
(c) Conditioning camera with FeatureNeRF
Figure 2: Overview. (a) The overall training pipeline. depicting how camera pose conditioning operates with two branches, the main and multi-view. (b) Visualization of our progressive attention mechanism. We gradually broaden the spatial attention field, enhancing geometric consistency. (c) The detailed illustration of the cosdiiindd transformer block. FeatureNeRF and projection layer are trained to produce feature map, obtained by concatenating the main-branch and multi-view feature map.
3 METHODOLOGY
In this section, first introduce multi-view customization task, explicitly incorporating camera viewpoint control (Sec. 3.T) Next, we describe pose-conditioned transformer blocks to reflect camera poses into the customized subject (Sec. 3.2). Then, we introduce video diffusion backhone designed for large viewpoint changes (Sec. 3.3). Finally, we present our core contributions - depth-aware feature rendering and consistent- aware latent completion
ensure multi-view consistency not only of the customized subject but also their surroundings under novel textual prompts (Sec. 3.4).
3.1 PROBLEM DEFINITION
We define multi-view customization as an extension of traditional customization that incorporates explicit control over camera viewpoints. Traditional customization aims to model the conditional distribution p(æ Y', c). where e is a textual prompt describing a novel concept and Y' {v/) are reference images. A common approach is textual inversion (Gal al. 2022), which introduces a learnable embedding vector that replaces part of the text prompt c(v). The embedding is learned by minimizing the denoising objective,
v = arg min, E, B=,c=~((001, [[|e eo (x;; c(v), 1) where t denotes the diffusion timestep.
In multi-view customization, each reference image is paired with its camera pose, Y {(3), m)),, The goal to model the conditional distribution
p(ag.M Y. c, fdom )
(1)
where æo:as = (mm)) m= denotes a set of generated images under target camera poses {0m). J. For brevity, denote the of multi-view outputs in the following sections. This formulation enables explicit camera pose control in addition to identity preservation and text alignment, thereby enhancing controllability. consistency, and realism of the generated results.
JM
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3.2 CONDITIONING CAMERA POSE IN DIFFUSION MODELS
To effectively learn the subject's geometry from reference data, we adopt the pose-conditioned transformer block from CustomDiiffusion360 (Kumari et al. 2024), replacing the original spatial transformer in the diffusion models. The transformer block defined (zo, {(z,, mz)) c, c) where 2o is the main- branch feature map and {Zz; } are reference features with corresponding poses.
The two branches play complementary roles:
Main branch. Generates target-view features for decoding into the final image. Its feature map is refined via self-attention s and cross-attention modules conditioned on C: Xx := g(s(zo), c)..
Multi-view branch. Aggregates reference-view features { computed as X; := [((((z))) c)). FeatureNeRF synthesizes a pose-aligned feature map X,, by combining {X;} with camera poses {Ti) via epipolar geometry (Yu et al., 2021) and volume rendering (Mildenhall et al. 2021):
Xy FeatureNeRF (XX,, m:)) c).
These feature maps are concatenated and projected into the backbone's 's feature space, shown in Fgg. 2a.
3.3 BACKBONE FOR DYNAMIC VIEW CHANGE
A pose-conditioned transformer block F pose generally produces consistent multi-view images about the subject, but novel surroundings or clothings are often become inconsistent across views. To address this, we repurpose video generation into niet generation based on AnimateDiff (Guo et al. (003)) inherently suited for handling viewpoint transitions. Our video denoising model D9 is defined as:
Dg : (#I.N; Y, c, @1.N) + 31.N.
(2)
mapping noisy inputs Ei.N clean frames LI.N. conditioned camera poses 01.N.
AnimateDiff's ID temporal attention limits its interactions to identical spatial positions, hindering effective induced displacements. We extend it with dense 3D spatio-temporal attention (STT) for richer context modeling. To preserve stability and pretrained knowledge, we gradually expand the spatial attention field of STT during training (Fig. 2)). The detailed design choices are discussed in [Sec. A
modeling of viewpoint- dieppoit-induueed
With this backbone, we fine-tune our customized model by incorporating textual inversion and a pose- conditioned transformer block, optimizing with standard denoising and additional FeatureNeRF losses (please see Sec. B for the details).
3.4 INFERENCE-TINE MULTI-VIES CONSISTENCY UNDER LIMITED DATA
Depth-aware feature rendering. Although our video backbone Sec. 3.3) produces coherent surroundings, it does not explicitly enforee geometric consistency under camera motion. To address this, we propose depth-aware feature rendering, which explicitly imposes geometric constraints conditioned on novel prompts during inference. Unlike previous depth-conditioned multi-view generation methods (Ren et al. 2025 Yu jet al. 2024), which rely on large-scale training data, our method effectively addresses the lack geometric supervision for novel prompt-driven content.
First, the anchor feature mesh M, is defined using an anchor frame I, selected from DI.N. denoted M. (Pa,
a Fa, Ta), where the anchor frame's 's feature map F, is directly used as texture of mesh! The vertices Pa E xW x3 are derived from the depth map D, estimated by an off-the-shelf depth
F. is the fcature map taken immediately before the spatial transformer in the second block Fig. 20), a feature level previously demonstrated to be effective for diffusion-based feature modification (Go el 2024
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N
C
Festure Restering
M
F
M
21
RiMar
RlMMa 9n))
DRPP Forward
F a
Latent Completion
Pa a
Ta
R
M
Ri.Ma. du)
F
M N
J1-1
(u) Depth-aware feature rendering
(b) Consistent-awann latent completion
Figure 3: (a) Anchor feature mesh Ma. consists of texture Fa, vertices P and triangles Ta is constructed using the feature and depth maps, and camera pose the anchor frame. The Ma is used to render the projected feature maps for the other camera poses. (b) Completion via latent perturbation for new visible areas.
estimator (Bhat et al. 2023) applied to a.. To align the estimated depth Ó with FeatureNeRF's geometric scale, we normalize and shift by the median depth demed of the anchor view: D f norm D + d The depth map D is resized the feature resolution rH Wr) of Fa. Using rotation R R3x3 translation E IR33 and intrinsic matrix K E 3x3 of the camera parameters associated with C. the 3D points are computed P R(DK - v, 1 + where [u, vj denotes a feature-space coordinate. Dense mesh triangles Ta are defined on the pixel grid using D, while pruning the regions that become newly visible from other viewpoints, yielding discontinuous mesh boundaries (see Fig. 3h, MMa).
Second, we render Ma for a given camera pose Φn. producing the rendered feature map Fy and visibility masks M Notice that the rendering is performed in the feature-space of F.:
n² M = R(Ma, on), 1 n S N, n + a,
where R denotes a differentiable mesh renderer.
Finally, during the first 35 steps of the 50-step DDIM sampling process, we update each feature map by replacing masked regions with rendered anchor features:
F M F (1 M.) O F., n n n a,
(4)
then, we substitute the combined feature map F for before the spatial transformer the second up- uloblock.
Consistent-awan latent completion. Regions where (1 - (1-M") is nonzero correspond to newly visible areas that requires content generation not present the anchor frame. To address this, we introduce consistent- aware latent completion, which leverages stochastic perturbations to synthesize these 'disoccluded' regions (see 3D). Specifically, given an intermediate noisy latent 2, the demoising process, we predict an initial latent 45 that is semantically meaningful yet incomplete. We then reintroduce noise into via the forward diffusion process, reverting to the original timestep and yielding a perturbed latent a/{- The disoccluded regions in the original latent 2; are selectively replaced with those from 2//, enforcing spatial coherence across frames through the temporal consistency of the video backbone. This procedure is itcratively conducted from timestep T down to early timestep (close T),, allowing semantic flexibility and coherent synthesis of novel details newly exposed regions. Further implementation details, including anchor mesh construction and inference pseudo-code, provided in Sec. B
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of multi-view
Inference conditioned camera and text deseription
reference images
Text prompt: "A V motoreycle in front of grafiti-covered urban wall.
Custom imn
Imnggo MY
Target poses
Qurs
set of multi-view images
Inference conditioned camera pose and description
prompt chair inside gr greenhouse with plants
Txt-blogen with DIB
Target camera poses
Ours
set of multi-view reference images
Inference conditioned on camera and text description
CustomDiiitsieme 160
Text prompt "A V car parked by the cliff."
Target poses
Our
Figure 4: Qualitative results. The light blue boxes indicate the multi-view view training dataset for the target concept, while the light pink boxes illustrate the inference phase, where results conditioned new text and target camera poses.
4 EXPERIMENT
4.1 EXPERIMENTAL SETUP
Dataset. We train our video diffusion backbone using subset (430K samples) of the WebVid10M dataset (Bann et al.) 202T). For customization experiments, use concepts selected from the Common Ob- jects in 3D (CO3Dv2) dataset (Reizenstein al. (02T). following the setup CustomDiffusion360 (Kumari et al. 2024). Specifically, we select four categories car, chair and motorcycle- with three concepts per category. For evaluation, we randomly sample camera trajectories from the CO3Dv2 test set as target camera poses.
Competitors. As our task is novel, we compare our proposed method against various applicable baseline approaches: (1) Custom img Img-MVgen: This method generates multi-view images by inputting single customized image into the image-conditionel multi-view generation model, SEVA (Zhou et al., 2025). The single input image is taken from the first frame of the output produced by our model, conditioned on the
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MV Generation
Customization
Inference Cost
Camera Pose
Multi-view Consistency (1)
Identity
Text Alignment (t)
Method
Accuracy (1)
Preservation (J)
Time (s)
GPU (GB)
Custom Img + Img-MV gen
0.675 + 0.12
0.214 + 0.15
0.504 + 0.12
0.676 = 0.11
96.18
6.73
Txt-MV gen with DB
0.283 1 0.25
0.116 1 0.09
0.557 1 0.12
0.723 1 0.10
27.20
5.42
CustomDiiffusio3300
0.000 1 0.00
0,190 1
0.417 1 0.12
0.10
74.97
4.99
MVCustom (Ours)
0.735 + 0.
0.121 + 0.10
0.448 + 0.11
0.744 + 0.10
130.92
19.29
Table 2: Quantitative comparison on multi-view generation, customization, and inference cost. We
highlight the best score in light red and the second-best in yellow.
target text and camera pose. (2) Txr-MVgen with DB: A text-conditioned cameraa-motion-conttlllaaal model, CameraCtrl (He et al. 2024), customized with the conventional DreamBooth-Loke (Ryu) (203) approach. (3) CustonDiffision An existing object
customization method (Kumari et al. 2024).
Further comparisons and detailed discussions regarding additional competitors' capabilities and limitations are provided Sec. C
Evaluation metries. We evaluate our method using four metrics: camera pose accuracy, multi-view consis- tency, text alignment, and identity preservation. Camera pose accuracy is measured as the average inter-frame relative rotation accuracy (range: [0, IJ), computed via COLMAP (Schonberger & Frahm 2016). If COLMAP fails to reconstruct camera poses, we assign the minimal accuracy score (0). Multi-view consistency is quanti- fied by visual similarity (Fu et al. 2023) across views, computed over all view pairs. Identity preservation measured via DreamSim similarity (Fu et al.) (2023) between generated outputs and reference images. Text alignment is evaluated using CLIP similarity scores between textual prompts and generated images. Further details and additional evaluations are provided in [Sec. C
4.2 RESULTS
As shown quantitatively in Tbl. 2 and qualitatively in Fig. 4 MVCustom the only approach that simultane- ously achieves high multi-view consistency and accurate customization fidelity. More comprehensive video comparisons can be found in the project page.
Multi-view consistency with perspective alignment. Accurately reflecting target camera poses is crucial for multi-view customization. As shown in Tbl. (camera pose accuracy) and qualitative examples (Fig. 4), MVCustom faithfully generates multi-view images aligned with specified viewpoints. In contrast, TV clearly observed
gen with DB fails to reflect rotation- in the chair example of Fig. 4 and confirmed by poor pose accuracy (Tbl. 2p. This indicates that the strong camera controllability in Txt-MV generation does not directly translate into multi-view customization through conventional tne-ttning (see [Sec. C.3). Similarly, Img-MV gen methods rely on single reference image, limiting subject appearance and geometry, and causing unnatural subject- surrounding relationships distant views (e.g., the motorcycle in Fig. 4). Although CustomDiiflssionnód maintains subject consistency, arbitrary surroundings across viewpoints yield poor holistic multi-view consistency, leading to COLMAP reconstruction failure and zero pose accuracy (Tbl. 2). By leveraging video backbone and inference strategies, MVCustom substantially improves holistic multi-view consistency and perspective alignment, outperforming all baselines.
trajectories despite explicit conditioning,
As shown in Tbl. 2 MVCustom requires higher computational resources primarily due to the external depth estimator (increasing GPU memory) and the feature replacement step (increasing inference time), unlike other competitors relying solely on denoising. Nevertheless, explicitly enforcing geometric consistency at inference is critical given the constraint of extremely limited training data. Thus, argue that significant
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atteation backbone (Ours) attention backhone
(a) Ablation for DFR and latent completion
(b) Ablation for temporal attention of video backbonc
Figure 5: Results of ablation studies. (a) Stepwise effect of applying depth-aware feature rendering (DFR) and consistent- latent completion under x-translation camera pose. (b) Impact of temporal attention on feature replacement. (i) Feature replacement vertically copies the feature map from frame to frame 2. Our method successfully enforces spatial flow, whereas ID temporal attention fails to capture the intended translation.
improvements in multi-view consistency, geometric accuracy, and customization fidelity clearly justify this computational trade-off.
ID preservation with text alignment The Custom ing + Img-MV gen baseline fails preserve subject identity and the textual description of surroundings, particularly as viewpoints move further from the input image (as shown qualitatively in Fig. 4). Tx--MV gen with DB also fails to retain the reference subject's s appearance and geometry, leading to poor identity preservation. In contrast, both CustomDiffusion:66 and our MVCustom method successfully preserve the reference subject and effectively reflect diverse textual prompts across all views, demonstrating superior customization fidelity.
4.3 ABLATION STUDY
Depth-aware feature rendering & Consistent-aware latent Completion. Customization fine-tuning alone yields static surroundings despite varying subject poses (Fig. 5h-i). Our novel depth-aware feature rendering enforces geometric consistency, enabling accurate spatial shifts (c.g., building position) according camera movements (Fig. 5)-i However, newly revealed regions reuse previous content, reducing realism. Thus, we propose latent completion, leveraging the generative power of our diffusion backbone to naturally synthesize previously unseen, context-appropriate details (Fig. 3d). Unlike conventional multi-view methods requiring extensive datasets, our method explicitly addresses data limitations in customization, significantly enhancing multi-view coherence and realism; see Sec. and Sec. E for additional ablation samples and completion results demonstrating visual diversity.
Spatio-temporal attention. We evaluate dense spatio-temporal attention's 's effectiveness for spatial con- sistency. As illustrated in Fig. 5p-i, we vertically shift and insert first frame's 's features into subsequent frames, expecting clear semantic translations. While original AnimateDiff with ID temporal attention fails to preserve spatial coherence due to limited pixel interactions (Fig. 5p-ii), our proposed spatio-temporal attention successfully maintains spatial consistency and semantic flow (Fig. 5-iii). Thus, integrated spatio- temporal attention is crucial for accurately modeling large view displacements and explicitly enforcing spatial constraints, especially when employing feature replacement (Sec. 3.4).
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5 CONCLUSION
In this work, we introduced the novel task of multi-view customization, integrating explicit camera viewpoint control, subject customization, and spatial consistency for both subjects and their surroundings. To address this task, proposed MVCustom, diffusion--ard framework leveraging dense spatio-temporal attention for robust multi-view synthesis. Additionally, we introduced two inference-stage deepegies-ddethh-wwaar feature rendering and consistent-aware latent completion-to explicitly enforce geometric consistency and faithfully generate disoccluded regions. Extensive comparisons show that MVCustom is the only approach that effectively integrates accurate multi-view generation and high-fidelity customization. We believe this framework provides foundation for future work on controllable and customizable multi-view generation.
Limitations and future work Our framework currently cannot alter the intrinsic object pose based text prompts during inference (e.g., changing from sitting to standing). This limitation arises because FeatureNeRF learns fixed canonical pose from reference images, and its radiance field does not take text prompts input conditions. Consequently, the object's intrinsic pose remains tied to this canonical representation. Experimentally, we found that injecting the rendered feature map X, via cross attention conditioned on textual prompts does not overcome this issue. Similar limitations related to intrinsic pose control are noted in prior customization work (Song et al., 2024). Future approaches might involve optimizing dynamic neural field conditioned on textual prompts built upon frozen static field from FeatureNeRF, using techniques such as score distillation sampling, or hypernetwork based methods. We leave these directions for future exploration. However, some text-specified shape variations can be reflected by adjusting the usage ratio of pose-conditioned transformer blocks during inference steps, as detailed in Sec. D.1
Additionally, another limitation arises from in- accuracies in the depth maps used in our depth- aware feature rendering. When the external depth estimator produces incorrect geometry, especially for reflective or textureless surfaces, our method directly constructs feature meshes using these inaccuracies. This limitation orig- inates from the external depth estimator rather than our framework itself. Similar issues affect other depth-conditionel methods (Yang et al.) 2025 Liu al. 2025) Hou Chen 2024) due to their inherent dependence accurate depth maps. Recent models (Yang et al. 2024; Min
Dopth Anything2
ZoeDoptt
Failure caso
Anchor frume
Estimated depth map
Generated multi-view images
Figure 6: Comparison of background perspective alignment in generated images depending on the quality of estimated depth.
ct al. 2025) have significantly improved depth estimation accuracy for reflective and textureless surfaces, suggesting potential mitigation of this issue. Fig. 6]demonstrates that accurate depth estimation produces realistic background geometry across multiple views: correctly estimating the depth of textureless wall ensures the building naturally rotates with the viewpoint change. Conversely, incorrect estimation perceiving the wall as distant background results in unrealistic backgrounds across views. In conclusion, expect that ongoing advancements in depth estimation techniques will soon overcome this limitation, enabling our framework produce even more realistic and consistent multi-view results.
Acknowledgements This work was supported by IITP grants [RS-2024-00439762, Developing Techniques for Analyzing and Assessing Vulnerabilities, and Tools for Confidentiality Evaluation in Generative Al Models] and [RS-2020-I120] 1120131,,, Artificial Intelligence Graduate School Program (Yonsei University)] funded by the Korean government (MSIT).
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Lukas Höllein, Aljaž Božič, Norman Müller, David Novotny, Hung-Yo Yu Tseng, Christian Richardt, Michael Zollhöfer, and Matthias Nießner. Viewdiff: 3d-consistent image generation with text-to-image models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5043-5052, 2024.
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Nupur Kumari, Grace Su, Richard Zhang, Taesung Park, Eli Shechtman, and Jun- Yan Zhu, Customizing text-to-image diffusion with camera viewpoint control. arXiv preprint arXiv:2404.1233. 2024.
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Fangfu Liu, Hanyang Wang, Weiliang Chen, Haowen Sun, and Yueqi Duan. Make-your-3ee Fast and consistent subject-driven 3d content generation. arXiv preprint arXiv:2400. 240.2044339.. 2024.
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Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1): 99-106, 2021.
Junhong Min, Jimin Kim, Cheol-Hui Min, Minwook Kim, Youngpil Jeon, and Minyong Choi. Depthfocus: Controllable depth estimation for see-through scenes. arXiv preprint arXiv:2511.16993, 2025.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference machine learning, pp.
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Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 10901-10911, 2021.
Xuanchi Ren, Tianchang Shen, Jiahui Huang, Huan Ling, Yifan Lu, Merlin Simier-David Thomas Müller, Alexander Keller, Sanja Fidler, and Jun Gao. Gen3e: 3d-informed world-consistent video generation with precise camera control. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 6121-6132, 2025.
Natamiel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfrr Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. Proceedings of the IEEE/CVF conference on computer vision and pattern recognilion, pp. 22500-22510, 2023.
Simo Ryu. Low-rank adaptation for fast text-to-image diffusion fine-tuning. Low-rank adaptation for fast lext-to-image difsusion fine-tuning, 3, 2023.
Johannes L Schonberger and Jan-Michael Frahm. Structre-frrommmotion revisited. In Proceedings of the IEEE conference computer vision and patlern recognition, pp. 4104-4113, 2016.
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Yizhi Song. Zhifei Zhang, Zhe Lin, Scott Cohen, Brian Price, Jianming Zhang, Soo Ye Kim, He Zhang, Wei Xiong, and Daniel Aliaga. Imprint: Generative object compositing by learning identity-preservinn representation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 8048-8058, 2024.
Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In European Conference on Computer Vision, pp. 1-18. Springer, 2024.
Luming Tang, Menglin Jia, Qianqian Wang. Cheng Perng Phoo, and Bharath Hariharan. Emergent corre- spondence from image diffusion. Advances in Neural Information Processing Systems, 36: 1363-1389, 2023.
Lehan Yang, Lu Qi, Xiangtai Li, Sheng Li, Varun Jampani, and Ming-Hsuan Yang. Unified dense prediction of video diffusion. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp- 28963-28973,, 2025.
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Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis. arXiv preprint 24Xi::2499.02045 2024.
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Ziyang Yuan, Mingdeng Cao, Xintao Wang, Zhongang Qi, Chun Yuan, and Ying Shan. Customnet: Zero- shot object customization with crtiable-vieppingg in text-to-image diffusion models. arXiv preprint arXiv:2310.19788 197844 2023.
Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang, Sheng Zhang, Xianghui Yang, et al. Hunyuan3d 2.0: Scaling diffusion models for high resolution textured 3d assets generation. arXiv preprint arXiv:2501. 12202, 2025.
Jensen (Jinghao) Zhou, Hang Gao, Vikram Voleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models. arXiv preprint arXiv:2503, 2025.
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A REASON FOR OUR DESIGN CHOICE
In this section, we clarify the rationale behind our architectural design choices.
A.1 U-NET-BASED DIFFUSION MODEL
We specifically choose a U-Net-based video diffusion model rather than recent DiT-based models, primarily for architectural compatibility with FeatureNeRF (Kumari et al. 2024), which serves the starting point of our method. DIT models rely on Conv3D-based VAE, merging spatial and temporal dimen- sions. Consequently, these models cannot guarantee consistent number of features per frame, crucial for accurate frame-level camera pose conditioning. In contrast, our U-Net-based model explicitly maintains per-frame feature maps, ensuring effective camera pose conditioning.
Target view sample in test set of reference dataset
joap rugged dirt road."
"A V beside field of blooming sunflowers."
W/O DreanBooth
Among available U-Net-based text-to-video models, we build upon AnimateDiff (Guo et al., 2023) due to state-of-the-art video generation capability and compatibility with diverse styl- izations such as DreamBooth (Ruiz al. 2023) and LoRA (Hu) ct al. 202 As illustrated in figure Al incorporating various Dream models significantly enhances style controllability without altering the customized object's is identity. For photo- realistic rendering, we integrate our customization model with Realistic for all experiments.
Reaslistic Visioon
Justification for U-Net over DiT in multi-view customiza- tion task While a U-Net-based backbone may slightly com- promise visual quality compared to transformer-base archi- tectures, our primary goal is accurate geometry learning and effective frame-level control under extremely limited data con- ditions. DiT-based video models typically require training additional conditioning network for precise frame-level camera pose control (Bai al.) 2025 Bahmani et al. 2025 Bai al. 2024). Such approaches rely heavily on large-scale datasets; for example, RelCamMaster uses 136K videos and AC3D em- ploys 65M videos. In contrast, our multi-view customization scenario provides only a single video sample (approximately 50 frames), insufficient to reliably train additional networks with- out severe overfitting and loss of controllability. Recognizing the fundamental limitations of DiT- architectures under these constrained conditions, we adopted U-Net architecture that maintains per-frame latent features. This choice enables us explicitly impose geometry constraints during inference using Feature Mesh Rendering, thus achieving robust viewpoint controllability and geometric consistency.
Toon eou
Figure A1: Results with different Dream- Booth models. Since our method keeps spa- tial transformer layers of the video back- bone architecture frozen, we can flexibly apply various publicly available Dream- Booth checkpoints. The figure shows im- ages generated using two different check- points: Realistic and Toon You?.
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A.2 NUMBER OF FEATURENERF MODULES.
The number of NeatureNeRF modules has trade-off between accurately preserving the identity of the reference object and effectively reflecting new textual descriptions. Increasing the number of transformer blocks with FeatureNeRI better preserves identity, as these modules emphasize the reference object's details, However, this approach makes the model less responsive to novel textual descriptions during inference, because the projection layers, after the concatenation of the multi-view and main branches, are biased towards the reference branch rather than the main branch which directly processes new text conditions. Conversely, decreasing the proportion of FeatureNeRF modules enhances the model's ability to reflect diverse textual prompts, but weakens identity preservation due to the reduced influence of the rendered radiance field from the reference object. Our choice of employing FeatureNeRF in 7 out of 16 transformer blocks represents a balanced compromise, ensuring both faithful identity preservation and robust adaptability new textual inputs.
B IMPLEMENTATION DETAILS
B.1 VIDEO BACKBONE.
We start from the 1D temporal attention model of AnimateDiff (Guo et al. 2023). To adapt this model as a backbone for our customization framework, we first reduce the number generated frames. Although AnimateDiff originally generates 16-frame videos, simultaneous generation of 16 frames in both the main and multi-view branches of our framework would lead to a memory shortage. Thus, we fine-tune the model generate 8 frames instead, preserving the original ID temporal attention structure. During this initial fine-tuning stage, only the temporal transformer modules are trained with a denoising loss, using the Adam optimizer learning rate of 1 10 for 100 steps.
Afterward, as described in Section 3.3 of the main paper, we gradually transition from sparse temporal attention to dense spatio-temporal attention in sparse-to-dense manner. Again, fine-tune only the temporal transformer modules during this phase. The resolution of the attention feature maps increases progressively from 2 to 25, doubling every 10k training steps. This incremental training interval remains constant even for resolutions below 64, allowing dense spatio-temporal attention to form more quickly at lower resolutions. Following AnimateDiff's original practice, domain adapters are attached only during training and removed afterward.
We sample a 430k subset of the WebVidIOM dataset (Bain et al. 2021.. specifically selecting videos with a dynamic score above 80, and train the model at resolution of 512 pixels. Training is performed using the Adam optimizer with a learning rate of x 10-4 and the DDPM scheduler. We use four NVIDIA A6000 GPUs for approximately one week, with per-GPU batch size 2.
Fine-tuning for model customization. We perform model customization on top of our video backbone equipped with the proposed spatio-temporal attention, generating 8-frame videos a resolution of 512 pixels. During customization, both the main and multi-view branches generate 8-frame videos. The dataset for each concept sourced from DiftuomDifuusion366 (Kumari et al., 2024). The trainable parameters include the concept-specific text embeddings optimized via textual inversion (Gal al. 2022), well as the NeRF MLP and projection layers of FeatureNeRF.
Following CustonDifflusion6ól at the end of customization training, each FeatureNeRF stores intermediate #RofarcnsDDauscct from the training dataset. While CustomDiffiuioon36 stores these inter- mediate feature maps at random timesteps, our method specifically stores them at timestep 10 (close to the clean-image timestep) out of the total 1000 timesteps.
feature maps (X,) xx), x)x=
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We adopt the loss weighting scheme from CustomDDiftusion360 for both FeatureNeRF and textual inversion, and training is performed using the DDPM scheduler. Fine-tuning each concept takes approximately one day on a single NVIDIA A6000 GPU, using the Adam&bit optimizer with learning rate of 1 x 10-4.
B.2 INFERENCE STAGE: DEPTH--WARE FEATURE RENDERING.
We describe the detailed procedure for constructing the anchor feature mesh used in our feature rendering method.
The texture C of the mesh is directly obtained from the anchor frame's feature map F..
The 3D vertices P = ()T are generated based on depth D, estimated from the anchor frame é. using an off-the-shelf depth estimator (Bhat et al. 2023). To align the estimated depth with FeatureNeRF's learned geometry, we scale it using the median depth dined computed from the central ray of the anchor frame, as D = D/ + dmeed Here, we initially use the median depth from the first FeatureNeRF view. If dmedd inaccurate, the position of the rendered object may not align with the object generated by FeatureNeRF for target camera poses, negatively impacting the perspective alignment the background harmonized around the FeatureNeRF-reutee -eendered object. To resolve this, conduct grid search within +40% range around dmedd selecting the optimal d'ned that minimizes the error between the object region of frames generated without feature rendering and the RGB mesh produced using D = D/IDI + "nedd The foreground object region is defined by the alpha mask rendered by FeatureNeRI.
The depth D is resized to match the feature resolution (Hr, Wr). Using rotation R E R3x3 translation T RR and intrinsic matrix K E 3x33 from the anchor frame, we define the 3D points P RP c + T, where P. DK [u, 1] and (u, v) are image coordinates at feature resolution (Hp, WF).
To define triangles T. we first create regular grid of triangles Tra based on D. We then exclude triangles corresponding to depth discontinuities, which represent regions not visible from the anchor view but potentially visible from other viewpoints due occlusions. Triangles validated using:
{
2 + 1, min(ca)et v (AD(E-))
(aD().)) By
v(I)=
%. otherwise
>
where = 0.05 is a threshold for significant depth variations. The final triangle set is then:
T= (1 E Traw
VV()) 1).
During mesh rendering R(M, Sn), lighting and shading effects are not considered.
This feature replacement is performed the second spatial transformer block in the U-Net decoder, specifically after the ResNet module and before feeding the feature map into the subsequent feed-forward network.
B.3 INFERENCE STAGE: CONSISTENT-AWER LATENT COMPLETION.
For the primary inference from pure noise IT to clean latent Tp. we use the deterministic DDIM scheduler (ODE). However, for creating perturbed latent o'y during latent completion, adopt the stochastic DDPM forward process (SDE manner). The timestep for latent completion is set at step 15 out of the total 50 inference steps.
We include pseudo-code in Algorithm D to illustrate the sequence of operations for depth-aware feature rendering and consistent-awan latent completion.
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Algorithm 1 Depth-aware Feature Rendering and Consistent-aware Latent Completion.
Require: RGB frames J1.N, feature maps FI:N, camera poses (#n]1: I:N, total diffusion timesteps tox 50, replacement diffusion timesteps fxep 35 Notation : For any quantity with subscript n (e.g. Ins Fas Φn), the index n E (I,...,,N) NN refers to the n-th frame. refers to selected anchor frame.
PART 1: Prepare Anchor Mesh function PREPAREAANCHORRESHCC F.... Kn, Tuay Ron))
dmed MEEDINNDEPHH(D)
D D/1D| + dmes; D
RESIZE (Hp, WF))
Pelu, u] + --(u,, u/ K
P(u, u) <- Rn, Pa(u, v] + T
Taw <- GRIDTRIANGLES((
T + E Tam min (() { M + MESH(P. (OSSH(PTT,HE F.T, Fn.) return M
PART 2: Inference stage with Depth-aware Feature Rendering & Replacement and Consistent-awars Latent Completion for too do
ift S tep then
na & CROOSEANCHORRRAMMER + DEPTHESTIMATOR Inn.)
(Kn., Toa: RNa) t ona for all n E {l,.... ......... N \ {na} } do
M + PREPAREANCHORMEES Fanchor Manchorr f- RENDER (M, 2n
D, Fr.: Kna Tmas Rma)
b Feature rendering
Fn t Maancho O Fanchor (1 Manchor ④ Fn
> Feature replacement
21 ENCODELATENT(AR + PREDICTCLEANLATENTTSS (I,)
2 DIFUUSONFORWARDPROSSSSS
E z O (1 Manchorr + De @ M Manchor
Completion for disocclusion
F, CICODELATENT(2nee))
DETAILS OF EVALUATION
C.I COMPETITORS
We provide detailed explanations regarding the evaluation setups and limitations of our main competitors, namely Brute Force (Customized single image SEVA) and CustomDiffusiio36000 Additionally, we include evaluation of CustomNet, another relevant method applicable to multi-vier customization.
Custom Img + Img-MV gen. We consider straightforward bascline, named Custom Img + Img-MV gen, which involves feeding single customized image reflecting text description into image-conditioned multi- view diffusion model. We specifically adopt SEVA (Zhou et al. 2025), the multi-vies diffusion model, for this baseline.
image-conditiona
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Although SEVA can accept multiple image inputs, achieving multi-view consistency among customized images that reflect novel textual descriptions remains challenging in multi-view customization tasks. Thus, this baseline uses only a single customized image as input to SEVA. The single customized image used as input is taken from the first frame generated by our method.
To evaluate Brute Force under the best conditions, we use the official target views provided by the SEVA implementation. Specifically, we select an "orbit" trajectory from the test set for camera pose evaluation, choosing "move-left" for positive x-translation and "move-up" for positive y-translation. We generate total of 34 frames from SEVA, from which 8 frames (including the input image as the first frame) are sampled for evaluation.
Txt-MV gen with DB. We trained a DreamBooth-LoRA (Ryu 2023 on Stable Diffusion using 50 reference images for 2000 steps, and then integrated the customized LoRA into CameraCtrl (He et al. 2024).
CustonDifffuson3660 Since CustomDiiffuion3300 (Kumari et al. 2024) is built on text-to-image model, the generated semantics differ significantly even with slight variations in camera pose, despite using identical noise and text prompts. Although the surrounding semanties may appear similar across different views, this similarity mainly results from partial overfitting to the prior preservation dataset. Thus, while CustomDif- fusion360 provides effective object pose controllability and customization capability, it does not explicitly address multi-view consistency.
We evaluate CustomDDiffusion366 using the official checkpoint provided in the original repository.
C.2 DETAILS OF THE QUANTITATIVE EVALUATION PROTOCOL
We evaluate our method on 14 concepts, each with 16 text prompts. We use the same set of evaluation prompts provided in the supplementary material of CustomDDiftusion3660 To ensure a fair comparison, all models share this common set of text prompts.
For each prompt, our method generates 8 images from different viewpoints. The target camera poses are randomly sampled from trajectories provided in the test set of the reference dataset.
MV-Consistency. To quantify the multi view (MV) consistency of generated images across viewpoints, we measure visual similarity using DreamSim. Similarly. we conduct additional analyses in table Al Juiing image- based similarity metries computed by CLIP ViT-B/32 (Radford et al. 2021) and DINO VIT-S/16 (Caron et al. 202T). We compute these metrics across all pairwise combinations of images generated from the same follow the official implementation, where lower values indicate higher perceptual similarity. For CLIP and DINO similarities, we extract features from generated images, with higher scores indicating better similarity. Our method consistently achieves the highest scores across all three metrics, demonstrating strong preservation of subject consistency in multi vietw images.
concept and textual prompt. For DreamSim,
Additionally, we evaluate geometric alignment using the Met3R metric (Asim et al., 2025). which quantifies the consistency of 3D structures and semantics between pairs of generated images from different viewpoints. Following the original Met3R protocol, we compute pairwise scores for all adjacent frame pairs and average them to obtain the final MV-consistency score. Lower Met3R scores indicate higher consistency. However, Met3R does not explicitly evaluate alignment to the target camera poses, as evidenced by favorable evaluations even when camera poses are completely disregarded, such as in Txt-MV gen with DB.
Camera Pose Accuracy. We evaluate camera controllability using Camera Pose Accuracy (CPA), nor- malized between 0 and 1. We exclusively focus on rotations since translation scales are inconsistent across methods: and CustomDiffusion336 (Kumari al. 2024) use normalized poses, while CameraCtrl (He
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Method
Met3R (L)
CLIP image similarity (1)
DINO image similarity (1)
Custom Img Img-MV gen
0.252 + 0.078
0.877 + 0.067
0.759 + 0.147
Txt-MV gen with DB CustomDiifusion3300
0.216 0.107
0.927 0.044
0.868 0.096
0.400 + 0.085
0.890 0.056
0.802 + 0.095
MVCustom (Ours)
0.265 + 0.154
0.933 1 0.048
0.868 = 0.097
Table A1: Additional quantitative evaluation of multi-view consistency. Our method achieves the highest multi-view consistency across all three image similarity metrics, demonstrating that the generated images exhibit strong alignment and similarity with each other across different viewpoints.
al. 2024 and SEVA (Zhou et al. 2025) do not. Direct translation comparisons would therefore confuse controllability with scale mismatches.
Given a target camera pose sequence Rg and estimated poses Ress obtained from COLMAP on the generated video, the angular deviation for each frame defined as:
tr tr Rienn)
0
arccos
0 E 1|
(5)
2
This angular error is converted into per-frame accuracy:
a² = Øi
a
E 00,1],
(6)
where a² = 1 indicates perfect alignment and a² = 0 corresponds to rotation difference of 180"
The sample-level CPA for a video with N frames is computed as:
N
CPAsapll
(7)
The final detaset-level CPA averages all M evaluation samples:
M
CPA final = Σ CPA
(8)
M
sample
We adopt the following failure handling strategy for robustness and faimess:
Full reconstruction failure: COLMAP fails entirely to reconstruct a trajectory due unsuccessful feature matching, we assign CPAs = 0.
Partial pose failure: COLMAP reconstructs partially but fails for certain frames, set those frames' accuracy to a² = 0, which contributes to the average CPA.
This ensures that the reported CPA accurately reflects controllable camera trajectories and penalizes both sequence-level and frame-level estimation failures.
Reference image fidelity. To evaluate how well the generated images depict the concepts present in the
reference images, we measure the perceptual similarity using DreamSim (Fu et al. 2023). Since DreamSim effectively captures semantic content, using subset of reference images yields results comparable using the full set. Therefore, for efficiency, we construct the reference set by randomly sampling reference images for each text prompt, matching the number of generated images. We then compute DreamSim between each generated image and all sampled reference images, and report the average similarity across all concepts and text prompts.
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conditioned pose lorger and lext description
conditioned pase and text description
Torget
Inference conditioned camera pase and text description
Torger cantern pour -Y insusiatioon
camerra pour ++7 trandattio
cantrey pose
prongt: I chair resting
Text prompt chair rooffop gurdew overfouking skyline. Only model customization
Oriö
Text proupt: 1 seddyybar holding" a bolloon on grazs meadow: Only model customization
or ffllff wubil carpet.
Only model customization
Depti-aware rendering
Depth-wware
Depthbaware Sulgy
Consistent-oe amarr latent completion
+ Condistent awnrre Lasent completion
Consiscent aware lotest completion
dn sughott
Figure A2: Results on ablation study.
Text alignment. CLIP text-image similarity is computed between each generated image and its correspond- ing prompt using the CLIP ViT-B/32 model (Radford et al. (222I). We compute the similarity between each generated image and its corresponding text prompt and report the average score as the final result. Higher similarity scores indicate better text alignment of the generated images.
C.3 LIMITATIONS OF STANDARD CUSTOMIZATION ON CAMERA-CONTROLLE MODELS.
Applying standard image customization methods (, DreamBoothh-oRR. to text-conditioned camera pose controllable models CameraCtrl) significantly reduces camera pose controllability.
Method
Rotation Error (L)
Translation Error (L)
CameraCtrl
15.660
4.385
CameraCtrl + DB-LoRA
16.500
4.608
Table A2: Effect of naive customization CameraCtrl. Evaluation follows the protocol of CameraCtrl: rotation error is measured degrees, and target poses are randomly sampled from its public trajectory set.
These results show that simply applying image customization to a text-conditioned multi-view generation model does not achieve multi-vier customization. Therefore, framework specifically designed for the goal of multi-view customization is necessary.
D FURTHER ABLATION ON INFERENCE STRATEGIES
This section provides additional analysis of our inference strategies: depth-aware feature rendering and consistent-aware latent completion. With only model customization (fine-tuning), the target camera trajectory aligns solely with the object, shown in the first of Fig. A2 while the surroundings remain static unrelated to the camera pose. Adding depth-aware feature rendering ensures that the surroundings accurately reflect the target camera trajectory, as illustrated in the middle of Fig. A2 This enhancement significantly improves perspective alignment, which quantitatively supported by the substantial improvement in multi- view generation scores in Tbl. A3 However, disoccluded regions retain previous content, lacking diversity.
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Multi-view Generation
Custemization
Method
of Colmap recon (f)
PoseAcc (1)
Multi-ier consistency (L)
Identity Preservation (.)
Text Alignment (1)
Only castamization
36,
0.543 0.179
0.095 0.067
0.355 0.076
0,682 0.074
Depth aware rendering
Consistent aware latent completion
45.38 26.39
0.771 0.142
0.113 0.081
0.38V 1 0.085
0,631 0,006
Table A3: Quantitative evaluation of inference strategies. "# of COLMAP recon" indicate the average number of reconstructed points from multi-view images with target camera poses by COLMAP. Best results are highlighted in bold; second-best in italic. Evaluations conducted on rotation-aware camera trajectory and translation trajectory.
Introducing consistent- aware latent completion further improves realism by adding contextually appropriate new content into disoccluded regions, as demonstrated the bottom row Fig. A2 While this addition slightly decreases the multi-view visual consistency metric due to reduced semantic similarity across frames, it does not imply visual inconsistency. The improved COLMAP reconstruction points and pose accuracy validate this.
D.1 ANALYSIS OF OBJECT SHAPE VARIANTS FROM TEXT PROMPT
We analyze the extent shape variation achievable by adjusting the usage ratio of trained pose-conditioned transformer blocks (containing FeatureNeRF) versus the backbone's 's original transformer blocks during inference denoising steps. Specifically, we investigate the following scenarios:
Case (a) 100% pose conditioning: Trained pose-conditioned transformer blocks are applied throughout all inference steps. This accurately maintains viewpoint alignment, preserves the reference object's s shape, and partially reflects shape variations specified by the text prompt.
Case (b) - -50 % pose conditioning: Trained cosdicondii transformer blocks are applied only during the initial 75% the inference steps, with the backbone's 's original transformer blocks used the remaining steps. This setting allows greater reflection of text-specified shape variations while slightly compromising reference shape consistency.
Case (c) - 50% % pose conditioning: Trained pose-conditioned transformer blocks are applied in the initial 50% of inference steps, and the remaining steps use the backbone's s original transformer blocks. This reflects detailed shape variations from text prompts while partially preserving key reference features (e.g., color or door shape), though viewpoint alignment begins to degrade.
Case (d) - 25 % pose conditioning: Trained pose-conditioned transformer blocks are applied only during the initial 25% of inference steps, after which the backbone's original transformer blocks are utilized. This configuration does not retain reference object details or viewpoint consistency, accurately reflecting only the general object type and details described by the text. Hence, it does not constitute meaningful reference customization.
These shape variations specifically affect only the customized object, leaving the surroundings unaffected.
DIVERSITY OF LATENT COMPLETION
our method, after constructing the anchor feature mesh from an anchor frame, we employ latent-leve completion naturally fill newly revealed disocclusion regions in other views. The stochastic noise introduced during the diffusion forward process generates a perturbed latent zy This ensures diversity in the semantics synthesized within these disoccluded regions.
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Razge cCising Be pole-coafttioee block 100%
Tarpel camera pose samples
Rarge of wiigg the pose conditioed bock 75%
Target camera pase samples
dusciiing
decennite
A joop 1 parked
jesp V parked
A vintage cur at at the desert.
vimtgee car parked the desert
trck
V
style parked the desert
parked at the
desert.
Croe (s): Using poseccnditiinal transfomne blocks 100% of the denoising steps
Casc (b): Using pos-conditional tnassfommr blacks 75% the danissing sieps
Rasae of csiiz Be palee coadiioond block
Tarpet camera pose sampples
Ranze ssing dae coadi block
Target cameern pase samples
A joep 1
jeup V car parked
parked at the
vintage parked the desert.
vintage packed the desert.
pickup truck V4 parked desert.
A pickup track style parked desert.
Case (c): Using pase conditinddddddd trammr blocks in 50% of the denoising sleps
Case (4): Using pose cos-ccoddiooned transformer blocks 25% the denoising steps
Figure A3: Object shape variants achieved by varying the ratio of pose-conditioned transformer blocks. The ratio indicates the proportion of inference denoising steps that utilize the trained pose-conditioned transformer blocks. For example, 100% means that trained pose-conditioned transformer blocks used throughout all inference steps, whereas 75% means they are used only for the initial 5 of steps, with the remaining steps employing the backbone's original transformer blocks.
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Text prompt: "A green V* chair next potted plant."
Inpointing region
Anchor frame
Anchor frame
Seed 3
Seed
Seed 4
Seed
Seed
Seed 5
Figure A4: Diversity of consistent-aware latent completion. The white regions in the top row denote completion areas. The variations across seeds reflect the diversity induced by noise randomness.
Figure A4 illustrates how the introduced noise leads to semantic diversity in filling disoccluded regions. As the viewpoint moves toward later frames, the downward translation of the chair reveals regions at the top that must be filled, as indicated by the white regions in the "completion region" figure A4 Depending on the random seed, different semantics emerge in these newly exposed areas, such picture frames hanging plants. This demonstrates the diversity achievable through noise-driven latent-level completion.
Diversity is essential in generative models it significantly impacts the quality and richness of the generated content. Deterministic approaches often struggle to produce sufficiently varied outputs. This limitation reduces their applicability in scenarios requiring realistic and diverse visual details. By performing completion the latent level, our method leverages the semantically rich and smooth representation space provided by pretrained diffusion models. Thus, our latent-level approach generates natural and semantically diverse details. This ensures realistic transitions and consistent semantic variation across multiple viewpoints.
F FACTORS AFFECTING VISUAL CONSISTENCY
this section, we discuss key factors affecting visual consistency in multi-view image generation. Firstly, employing dense camera trajectories is essential to avoid visual inconsistencies. Attempting to cover wide camera trajectories using a limited number of views results in significant viewpoint differences between frames, causing visual artifacts. Secondly, observed that setting the classifier-free guidance (CFG) scale too high (approximately 7.5) significantly contributes to flickering artifacts. Thus, selecting an appropriate CFG scale crucial. An optimal CFG scale of around effectively mitigates flickering while avoiding blurry or noisy images. In conclusion, using denser camera trajectories and carefully choosing the CFG scale significantly reduces visual inconsistencies and improves overall multi- view quality.
G BROADER IMPACTS
Multi-view content generation and customization rapidly growing markets, each projected reach multi- billion-dollar scales by 2030. Currently, commercial Al-driven tools typically focus either on single-view customization multi-view visualization separately, forcing industries requiring both depend expensive
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and labor-intensive manual workflows. Our proposed multi-view customization framework directly addresses this industrial need by simultaneously enabling balanced performance in customization fidelity and multi-view consistency. By integrating these capabilities, our method can significantly enhance user engagement, sales conversions, and user experiences in fields such as e-commerce, advertising, and virtual reality.
However, as with many generative models, there exists the risk of misuse for malicious or deceptive purposes, such as generating misleading visual content. To mitigate this risk, we restrict our implementation to publicly available, research-focused models that have been released for responsible use. Additionally, our method does not involve training releasing any models that could produce NSFW sensitive content, thereby reducing the likelihood of generating harmful material.
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Reviews:
[{"review_id": "tlp9cBRsnB", "reviewer": "Reviewer_azR9", "summary": "This manuscript presents a joint multi-view generation with explicit camera pose control and subject customization task, and a corresponding framework to solve this task. More specifically, the authors proposed an MVCustom framework, which based on a diffusion model to generate multi-view images controlled by explicit camera poses with a consistent subject from given reference images. MVCustom is based on AnimateDiff and incorporates an additional feature NeRF and dense spatio-temporal layers to achieve multi-view consistency and subject consistency. In addition, the authors also introduce a depth-aware feature rendering to better achieve geometric consistency, and a consistent-aware latent completion technique to ensure the appearance consistency of both the subject and the surroundings.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The newly proposed task of jointly performing subject customization and multi-view image generation is a direction that has been rarely explored.", "weaknesses": "1. The reviewer is concerned about the practical application value and novelty of the proposed multi-view customization task. This task can be framed as a sparse-view version of controllable (camera pose-conditioned) video customization. The paper’s own choice of a video diffusion backbone (AnimateDiff) and its method of \"enhancing temporal coherence into holistic-frames consistency\" further reinforces this similarity. The practical distinction and added value over existing controllable video generation or customization frameworks are not sufficiently justified.\n\n2. The framework is built on a U-Net-based video diffusion model (AnimateDiff), which is arguably dated compared to recent transformer-based architectures. While the authors justify this in the appendix (Sec. A.1) as a compatibility choice with FeatureNeRF and frame-level precise camera control, the quality of the generated images is still highly constrained by the base model. Meanwhile, the core mechanism for camera control (pose-conditioned transformer block) is admittedly adopted from a previous work, further limiting its novelty. The other two main contributions, depth-aware feature rendering and consistent-aware latent completion, are presented as inference-stage techniques but lack rigorous quantitative validation.\n\n3.\tAs for the quantitative results shown in Tab. 2, the quantitative results in Table 2 do not show clear and comprehensive superiority, but a mixed trade-off rather than a decisive win, making the paper's claim of being the \"only one that achieves consistently strong performance\" an overstatement. Meanwhile, while the figures (e.g., Fig. 4) show improvements over baselines, inconsistencies are still apparent. This is particularly noticeable in the supplementary videos, where flickering and geometric instability suggest that holistic multi-view consistency is not fully solved, especially for the synthesized backgrounds that are not geometrically constrained by the FeatureNeRF model (explicit 3d modeling). To more robustly evaluate the claimed multi-view and geometric consistency, the authors should attempt to reconstruct an explicit 3D model (e.g., a mesh or Neural Radiance Field) from the generated multi-view outputs. This would provide a much stronger and more objective measure of 3D consistency than 2D metrics.\n\n4. The ablation study in Section 4.3 is insufficient as it is purely qualitative. The paper introduces several key techniques (Depth-aware Feature Rendering, Consistent-aware Latent Completion) but fails to provide any quantitative analysis to demonstrate their individual impact on the final metrics (e.g., in Table 2). Figure 5 offers only a single visual comparison, which is not enough to fully validate the effectiveness of each proposed module.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "MLEFSl2cU7", "reviewer": "Reviewer_QASA", "summary": "This paper introduces a diffusion-based framework named MVCustom, which is designed for multi-view customization with explicit camera pose control. For the training stage, it employs a video-diffusion backbone with spatio-temporal attention. To address the challenge of limited training data during inference, the paper proposes two novel techniques: depth-aware feature rendering to enforce geometric consistency and consistent-aware latent completion to realistically synthesize content in newly revealed regions.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper defines a novel task of multi-view customization that combines camera control with subject personalization.\n2. The proposed inference-time techniques for ensuring multi-view consistency effectively address the challenge of limited training data.\n3. The paper is well-structured and easy to follow.", "weaknesses": "1. Could you provide more details on the inference time of MVCustom compared to the baselines? The inference process, particularly the depth-aware feature rendering component, appears to be computationally intensive.\n2. On the project page, some results still show obvious hallucinations or inconsistencies when the viewpoint variation is large. Is there further analysis on this specific limitation? A discussion or visualization of typical failure cases would be helpful to understand the method's boundary conditions better.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "y5SkX9bSJE", "reviewer": "Reviewer_yETe", "summary": "This paper presents MVCUSTOM, a  diffusion-based framework explicitly designed\nto achieve both multi-view consistency and customization fidelity. Specifically, they propose a novel task, multi-view customization, and  address multi-view consistency and limited data problem in customization.", "questions": "see weakness", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "• The paper is well-written with a logical structure that makes the technical contributions easy to follow.\n• The proposed framework integrating dense spatio-temporal attention modules into video diffusion-based backbone is well-justified and addresses limitations in multi-view consistency via temporal coherence.", "weaknesses": "1The author only conduct experiments on a U-Net-based video diffusion model, I wonder if the proposed method can further improve performance on DiT-based models.\n\nSuggestions:\n1.\tConduct experiments on DiT-based backbone like Wan2.1?\n2.\tInclude a comprehensive supplementary video showing: Side-by-side comparisons with all baselines (at least 3-5 examples per method)\n3.\tAdd more dense camera pose trajectory to show more consisent.\n4.\tCreate a project page with interactive demos which allow users to adjust the camera parameters.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "3UmRhtyb7N", "reviewer": "Reviewer_w9qo", "summary": "This paper introduces multi-view customization, a novel task for generating multi-view consistent, customized subjects in new, text-described scenes. The proposed method, MVCustom, uses a video diffusion backbone and two novel inference-stage techniques: DFR for geometric consistency and CLC to realistically fill disoccluded regions.", "questions": "1. What is the quantitative inference latency of the full MVCustom pipeline compared to baselines?\n2. How robust is DFR to significant failures from the monocular depth estimator?\n3. Can the text prompt influence the subject's intrinsic pose at all, or is it completely fixed by the reference images' canonical pose?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. Defines a novel, challenging, and high-impact task at the intersection of customization and 3D-aware generation.\n2. Presents a highly innovative inference strategy (DFR+CLC) that cleverly enforces geometric consistency on new, text-driven scenes using 2D priors (depth) and feature rendering.\n3. Provides compelling experimental validation (especially in the supplementary videos) demonstrating clear superiority over strong baselines in holistic (subject + background) consistency.", "weaknesses": "1. Inference Cost / Latency: The proposed inference pipeline is considerably complex, involving multiple forward passes (for CLC) , depth estimation, mesh construction, and differentiable rendering (for DFR). This likely results in significant inference latency compared to standard diffusion models. The paper does not discuss or quantify this computational overhead, which is an important factor for assessing the method's practicality.\n2. Rliance on External Depth Estimator: The efficacy of DFR is dependent on the accuracy of an off-the-shelf monocular depth estimator. It is well-known that such estimators can be unreliable on transparent, reflective, or texture-less surfaces. While the paper mentions aligning the depth scale (Appendix B.2), it does not deeply explore the impact of depth estimation failures on the final generation quality.\n3. Limitation on Object Pose: As noted in Appendix C, the method cannot alter the subject's intrinsic pose (e.g., from a \"sitting\" to a \"standing\" teddy bear) because FeatureNeRF learns a canonical pose from the reference images. This is a reasonable limitation, but given its importance, this discussion should be moved from the appendix to the main paper's limitations section.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)