Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Authors: Haoyu Wu
Diankun Wu
Tianyu He
Junliang Guo
Yang Ye
Yueqi Duan
Jiang Bian
Venue: ICLR 2026 Poster (2026)
Keywords: Generative Model; Video Generation; World Modeling
Primary area: generative models
TL;DR: We propose Geometry Forcing, a method that enhances video diffusion models by aligning their latent representations with pretrained geometric features to improve 3D consistency and visual quality in generated videos.

Abstract: Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models and the underlying 3D nature of the physical world, we propose Geometry Forcing, a simple yet effective method that encourages video diffusion models to internalize latent 3D representations. Our key insight is to guide the model’s intermediate representations toward geometry-aware structure by aligning them with features from a pretrained geometric foundation model. To this end, we introduce two complementary alignment objectives: Angular Alignment, which enforces directional consistency via cosine similarity, and Scale Alignment, which preserves scale-related information by regressing unnormalized geometric features from normalized diffusion representation. We evaluate Geometry Forcing on both camera view–conditioned and action-conditioned video generation tasks. Experimental results demonstrate that our method substantially improves visual quality and 3D consistency over the baseline methods.

Published as a conference paper at ICLR 2026
GEOMETRY FORCING: MARRYING VIDEO DIFFUSION AND 3D REPRESENTATION FOR CONSISTENT WORLD MODELING
Haoyu Wu'; Diankun Tianyu J Junliang Guo', Yang Ye',
Yueqi Duan Jiang Bian"
Microsoft Research
Tsinghua University
ABSTRACT
Videos inherently represent 2D projections a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely raw video data often fail to capture meaningful geometric-aware structure in their learned repre- sentations. To bridge the gap between video diffusion models and the underlying 3D nature of the physical world, we propose Geometry Forcing, a simple yet effective method that encourages video diffusion models internalize 3D repre- sentations. Our key insight is to guide the model's intermediate representations toward geometry-aware structure by aligning them with features from geomet- ric foundation model. To this end, we introduce two complementary alignment objectives: Angular Alignment, which enforces directional consistency via cosine similarity, and Scale Aligmment, which preserves scale-related information by re- gressing geometric features from normalized diffusion representation. We evalu- ate Geometry Forcing on both camera niew-conditioned and action-conditioned video generation tasks. Experimental results demonstrate that our method sub- stantially improves visual quality and 3D consistency over the baseline methods.
Alignment Loss
Diffusion Loss
Scale Algement
AngularAlggmeet Alinmmont
Video Diffusion Models
(b) Video Generation
VGGT Backbone
(a) Geometry Forcing (GF)
(c) Underlying 3D Representation
Figure 1: Geometry Forcing equips video diffusion models with 3D awareness. (a) We pro- pose Geometry Forcing (GF), simple yet effective paradigm to internalize geometric-aware struc- ture into video diffusion models by aligning with features from a geometric foundation model, i.e., VGGT (Wang al. 2025b). (b) Compared to the baseline method (Song al. 2025), our method produces more consistent generations both temporally and geometrically.
(c) Features learned by the baseline model fail reconstruct meaningful 3D geometry, whereas our method internalizes 3D representation, enabling accurate 3D reconstruction from the intermediate features.
"Equal contribution.
1 Project lead.

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INTRODUCTION
Learning to simulate the physical world and predict future states is a cornerstone of intelligent sys- tems (Ha & Schmidhuber, 2018). Recent advances in generative modeling (Ho et al. 2020 Rom-
bach et P & Xie 2023 Brown et al. 2020), coupled with the availability of large-
Peebles
scale video datasets, have led to significant progress in generating realistic visual environments
conditioned on text descriptions (OpenAT, 2024; Yang et al., 2024 Polyak et al.) 2024 Google
2025) agent actions (Hu et al., 2023; Guo al. 2025 Bar al. 2025). However, these ap- proaches typically aim to model pixel distributions across video frames, overlooking a fundamental
principle: videos are 2D projections of a dynamic 3D world (Glassner 1989). By focusing solely on image-space generation, such models often struggle to maintain geometric coherence and long- term consistency, particularly in autoregressive settings where small errors can accumulate over time (Chen et al. [2224a] Cheng et al.. 2025 Huang et al. 2025b).
Building on this motivation, growing line of research has explored explicitly modeling the dy- namic 3D structure of the physical world (Zhu et al.) [024 Aether et al.) 2025 2023]Jiang et al. 2025). as opposed to implicitly learning distributions 2D pixel space. For example, /Zang al. (2025a) proposes transforming 3D coordinates into point maps and jointly modeling the RGB and 3D in- formation. While effective to some extent, representing 3D information in a tractable form remains challenging, and the reliance on additional annotations imposes limitations on scalability.
In this work, we aim to bridge the gap between video diffusion models and the underlying dynamic 3D structure of the physical world. We begin with a fundamental question: Can video diffusion models implicitly learn 3D information through training on raw video data, without explicit 3D supervision? To investigate this, we analyze pretrained video diffusion model (Song et al. 2025)
by introducing a DPT (Ranftl et al.) (202T) head that maps its intermediate features to corresponding
depth maps (Wang al. 2025b). As illustrated Fig. [c). we observe that features learned solely from raw video data fail to yield meaningful geometric representations, highlighting a potential gap in the geometric understanding of video diffusion models trained without additional guidance.
To address this limitation, we propose Geometry Forcing (GF), simple yet effective approach that encourages video diffusion models internalize 3D representations during training. Inspired by recent advances in semantic REPresentation Alignment (REPA) for image diffusion models (Yu al. 2024a), we align features of video diffusion models with the geometric representations from pretrained 3D foundation model (Wang et al. (2055)). To align these two representations, our method introduces two complementary alignment objectives: Angular Alignment and Scale Align- ment. Angular Alignment enforces directional consistency between the diffusion model's features and geometric representations by maximizing their cosine similarity. Scale Alignment, in contrast, preserves the scale information of the geometric representations by predicting geometric features from normalized diffusion features. The decoupled formulation of Angular and Scale Alignment allows the model capture both directional and scale-related aspects of geometry, while improving stability during training and expressiveness in the learned representations.
We evaluate the effectiveness of GF on two widely adopted benchmarks: camera view-conditioned video generation on RealEstate 10K (Zhou et al. 2018) and action-conditioned video generation on Minecraft environment (Baker et al. 2022). Experimental results demonstrate that our method delivers substantial gains in geometric consistency and visual quality over the baseline methods. For example, GF reduces the FVD from 364 to 243 on RealEstate 10K benchmark. Moreover, the ability to reconstruct explicit geometry during inference opens up opportunities for integrating structured memory into long-term world modeling.
RELATED WORK
2.1 INTERACTIVE WORLD MODELING
A world simulator seeks model the underlying dynamics of the physical world by predicting future states conditioned on current observations and conditions. We review prior works through the lenses of interactive video generation, 4D generation, and consistent world modeling.
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Interactive Video Generation.
Recent advancements in generative models (Ho al. 2020 Rom-
bach et al. 2022 Peebles & Xie 2023 Lipman et al. 2023 Bruce et al. 2024 Parker-Holder et al.
2024
promising approach to world modeling. Beyond text-to-video synthesis (Chen et al. 2023; 2024b Kong et al,
Alosso et al. 2024
Valevski et al. 2024), have positioned video generation
2024 Wan al. 2025 Li al. 2024 Liu et al., 2025a Ye et al. 2025). interactive video genera-
tion
Yu et al. 2025b) that emphasizes responding interactive control signals evolves rapidly. Exist-
ing models incorporate different signals like camera controls (He al.
2024 Yu al. [024bb Song 2025) and action controls (Decart et al. [2024] Guo et al. 2025 Feng et al. [0224] [Shin et al.,
et al.
2024 Building on this progress, our work introduces novel training pipeline that enhances 3D consistency in video generation, enabling more coherent and realistic simulation of spatial scenes.
Interactive 4D Generation, In contrast to data-driven video simulators, 4D-based simula-
tors (Chung
2023 Bahmani et al., 2024b Wu al. 2025b; Yu al. 2025a Lee et al.
2023] Mildenhall et al. [02 Xiang 2003). these methods evolve from dynamic objects (Xu et al.] [2024] Bahmani et al.) (224a) to complex dynamic scenes (Niemeyer
2024 expliitlly model dynamic 3D structures (Kerbl el al.,
al.
2025). Building upon 3D content generation Raj
Geiger 202T Zhu et al. 2024). Recent works integrate video priors to improve the realism and
temporal coherence of 4D (Aether et al. 2025) Jiang et al. [025 Mai et al. [025 Chen et al. 2025)
For example, TesserAct (Zhen et al. 2025) predicts RGB, depth, and surface normals to reconstruct temporally consistent 4D scenes. While our work shares the goal of unifying 3D and video gener- ation, differs by injecting 3D geometric priors into video representation to improve temporal and spatial coherence.
Consistent World Modeling. A key challenge in world modeling lies in maintaining consistency over long video sequences. To address this, prior works have explored different solutions. Frame- level context mechanisms (Chen al. 22024a] Fuest et al. 2025 Po et al. 2025 Wu al. (025c) improve consistency by training with noisy context frames. Meanwhile, other methods leverage 3D information. For example, Xiao al. (2025) maintain memory bank indexed by field-of-view
overlap to retrieve relevant historical frames. Zhang et al.) (2025a) propose jointly modeling RGB frames and point maps maintain 3D consistency. In contrast, propose to directly internalize 3D representations into video diffusion models, enabling more stable geometric consistency.
2.2 3D FOUNDATION MODELS
3D foundation models (3DFMs) (Li et al. 2025 Yang et al. 2025 Smart et al. 2024 Wang* et al,
2025 Wang et al. 2024) have recently shown remarkable progress, applying end-to-end framework with fast and robust inference. These models are capable of predicting different 3D properties including camera poses (Zhang et al., (0255), depth maps (Piceinelli et al. 2024). and dense point clouds (Wang et al. 2025b), directly from visual inputs.
Due their accuracy, efficiency, and robustness, 3DFMs are becoming essential for enabling down- stream tasks like spatial reasoning (Wu et al.] 2025a; Huang et al., 2025a Fan et al.] (202)). au- tonomous driving (Fei et al.) (024). SLAM (Liu al. [2025b] Maggio et al. (025). and beyond. Inspired by their strong 3D capabilities, we explore incorporating 3D representations into video diffusion models to enhance temporal and spatial consistency for world modeling.
PRELIMINARIES
Our approach builds upon autoregressive video diffusion models (Chen et al. 2024a Song et al.
2023 Cheng al. 2025) and incorporates a 3D foundation model (Wang et al. (025b) into the training process to guide geometric learning. In this section, we provide a brief overview of both components to establish the foundation for method.
3.1
AUTOREGRESSIVE VIDEO DIFFUSION MODELS
Training. We formulate training pipeline based on Flow Matching (Lipman al. 2023 Liu et al.
2023) with Transformer backbone (Vaswani et al. 2017 Bao et al. 2023). aiming for simplicity and scalability. Let x {æ1,..., denote video sequence sampled from the data distribution,

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assign independent timestep for each frame t {ti,.... tr} and corrupt frames via interpolation:
= t.) t C2. where G ~~ (O,
The target velocity field is defined as the difference between noise and clean input. We train a neural network Up to minimize the Flow Matching loss:
PrM ||00 (E
Sampling. At inference time, the sampling follows a simple probability flow ODE:
dx uy (x* dt.
In practice, we iteratively apply the standard Euler solver (Euler 1845) to sample data from noise.
For autoregressive generation, we initialize the inputs with clean context and generate subsequent frames sequentially, conditioning each prediction on the previously generated frames.
3.2 VISUAL. GEOMETRY GROUNDED TRANSFORMER
Visual Geometry Grounded Transformer (VGGT) (Wang et al. 2025b) is feed-forward model that directly outputs 3D attributes of a scene, including camera parameters, point maps, depth maps.
VGGT has a Transformer backbone and multiple prediction heads. The model employ Alternating- Attention mechanism that interleaves frame-wise self-attention and global self-attention to extract local and global information. For each frame, local and global features integrated into unified representation, which is subsequently processed by task-specific heads to produce 3D attributes. We leverage the features from the Transformer backhone of VGGT to extract geometric representation.
GEOMETRY FORCING
4.1 MЕTHOО OVERVIEW
Motivation. Recent advances in video diffusion models have enabled the simulation of the world directly from large-scale video datasets. However, these models often overlook fundamental prop- erty of visual data: videos are 2D projections of an dynamic 3D world. To address this, we seek to narrow the gap between video diffusion models and the dynamic 3D structure of the world.
Observation. We begin by examining whether video diffusion models are capable of implicitly leamning 3D information when trained solely probe the geometric content of their learned representations, we adopt a strategy inspired by linear probing (He al., 2020h: we freeze the parameters of pretrained video diffusion model (Song et al. 2025) and train a DPT (Ranftl et al., 202T) head to map intermediate features to corresponding depth map (Wang al. 2025b). This allows us assess the extent to which geometric information is encoded in the model feature space. The results, presented in Fig. [c), indicate that features learned solely from raw video data do not produce meaningful geometric representations, suggesting a limited capacity of the model to encode dynamic 3D structure without explicit geometric guidance.
video data, without explicit 3D supervision. To
Challenge. Bridging the gap between video diffusion models and the dynamic 3D structure of the world presents significant challenges, primarily due to the limited annotated 3D data. A straightfor- ward approach is to jointly model RGB and geometric information within an end-to-end architecture. However, relying heavily on 3D annotations can hinder the scalability and generalization ability of the models, particularly when applied to large and diverse real-world video datasets.
In this work, inspired by recent advances in REPA YYu et al. 2024a), we propose Geometry Forc- ing (GF) that aligns the features of video diffusion models with geometric representations, encour- aging the model to internalize geometric information. Our approach builds upon video diffusion models deseribed in Sec. 3.1 In Sec. 4.2 we introduce two regularization objectives designed to fa- cilitate representation aligument between the diffusion model and geometric foundation model. The overall training objective, along with additional functional extensions, summarized in Sec.
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4.2 GEOMETRIC REPRESENTATION ALIGNMENT
To improve the geometric consistency of the learned representations, we introduce two complemen- tary alignment objectives: Angular Alignment and Scale Alignment. These objectives are designed to align the latent features of the diffusion model with intermediate representations from a pretrained geometric foundation model (Wang et al. 2025b), ensuring both directional consistency and scale preservation of geometric features within the feature space.
Angular Alignment. Angular Alignment enforces directional correspondence between the hid- den states of the diffusion model, denoted by h, and specified target features, denoted by y. We select intermediate features from the Transformer backbone of VGGT (Wang al. 2025b as these features preserve both local and global information within each frame and can be fur- ther used reconstruct various explicit geometric representations. E IR N'x Px PxD, where L denotes the number of layers, N denotes the number of input images, P denotes the patch count, and D denotes the feature dimension. To achieve Angular Alignment, we first use lightweight projector fe to map the diffusion latents Nx D to y shape. The Angular Alignment loss is then defined as:
y,
practice, the target features
L LNP ΣΣΣ cos (ye.pp fo(hn.)))
N
Cangular
where cos(:;) denotes cosine similarity. This loss aligns hidden states independently both the frame and patch levels. Since the VGGT backbone already incorporates cross-frame attention, do not explicitly enforce global alignment across frames in the loss.
Scale Alignment. While Angular Alignment ensures directional consistency, it disregards feature scale that could also encode geometric information. Although direct mean squared error (MSE) loss could supervise magnitudes, it often leads to optimization instability and model collapse due inherent scale difference across models. To address this issue, we introduce Scale Alignment, which preserves scale information through predicting the scale of target features given normalized diffusion hidden states. Specifically, we first normalize fo to unit length. Then we use another lightweight prediction head Ir to predict the full target features from normalized inputs:
(a((in))
hemp
Dt,n.p = D1p (hr...))
The Scale Alignment loss defined as:
N ΣΣΣ n=1p=1
Sscale
lue...
Ve.mpl1..
LNP
This decomposition stabilizes training while capturing both directional and scale attributes of geo- metric representations.
4.3 3D-RWARE AUTOREGRESSIVE VIDEO DIFFUSION MODELS
Building the autoregressive video diffusion framework and the proposed alignment objectives, we now present the overall training objective:
L = LFM + Anguular LAngular + Aseale L
Given the intermediate features of our model are well-aligned with geometric representations, an appealing consequence is the model's ability to predict explicit 3D geometry during inference. This enables unified generation of both video and 4D, effectively bridging the gap between videos and the underlying dynamic 3D structure the physical world, as illustrated in Fig. [ Moreover, the ability reconstruct explicit geometry during inference provides structured and interpretable form of memory, which can be further utilized to support long-term world modeling. We leave the exploration of such geometry memory mechanisms promising direction for future work.
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Table 1: Quantitative comparison on the RealEstate dataset for both short-term (16-Frame) and long-term (256-Frame) video generation. Our method (Geometry Forcing) achieves the best performance across all metries. bold values denote the best, and Underlined values indicate the second best. indicates the method is conditioned on the first frame only.
Method
Frames
FVD.
LPIPS!
SSIM
PSNR
RPEL
RVEJ
DFoTT (Song al. 2025
252
0.40
0.50
14.40
REPA Yuu al. 2024a
221
0.37
0.54
15.20
VideoREFA Zhang of al. 2025c
210
0.37
0.54
15.20
Geumetry Forcing (ours)
193
0.32
0.58
14.70
Geometry Forcing (ours)
REPA
16
179
0.34
0.54
15.00
Cosmos Agarwal
2025
934
0.68
0.20
10.25
DFoT Song et al. 2025
256
364
0.55
0.36
11.40
0.3575
297
REPA Yu al. 2024a
256
297
0.54
0.36
11.51
0.3337
315
VideoREPA Zhang ei al. 2025c
256
455
0.56
0.35
11.50
0.3823
190
Geometry Forcing (ours)
256
243
0.51
0.38
11.87
0.3337
272
Geometry Forcing (ours) REPA
256
237
0.51
0.37
12.10
0.3264
236
Discussion. Teacher Forcing (Williams Zipser 1989) is a widely adopted training paradigm
for autoregressive models (Radford et al,] 2019; Brown et al. 2020 Kondratyuk et al. 2024). To combine autoregressive nature with diffusion models, Diffusion Foreing (Chen et. (024a)) propose to train video diffusion models with independent noise levels for each frame. More recently, Self Forcing (Huang et al. 2025b) is proposed to address exposure bias in autoregressive video diffusion models. Orthogonal these methods, Geometry Foreing focuses improving the structure of the learned representations by aligning the intermediate representation of video diffusion models with geometry- signals from 3D foundation model. Our approach provides structural supervision at representational level, encouraging the model to internalize 3D consistency throughout training.
5 EXPERIMENTS
In this section, we evaluate Geometry Forcing (GF) on camera view-conditioned video generation on RealEstate 10K (Zhou al. 2018) dataset and oction-conditioned video generation on Minecraft
environment (Baker et al. 2022). We also provide more illustration and visualization in Appendix.
Implementation Details. For camera niw-ccodittoneed video generation, we apply GF on Diffu- sion Forcing Transformer (Song et al. 2025), training on 16-frame 256x256 videos for 2,500 steps with learning rate of 8 10 and batch size 8. Inference used first frame and per-frame cam- era poses. For action-conditioned video generation, we apply GF to Next-Frame Diffusion (Cheng eet al. 2025), training on 32-frame 384 videos for 2,000 steps with learning rate of 6 x 10 and batch size 32. We set Angular = 0.5 and ASeale = 0.05 to balance each loss component. All experiments are conducted on NVIDIA A100 GPUs.
Evaluation Metrics. We evaluate visual quality using FVD (Fréchet Video Distance) et al. 2018), PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index) (Wang et al. and LPIPS (Learned Perceptual Image Patch Similarity) (Zhang et al. 2018).
(Unterthiner
2004
To further evaluate geometric consistency, we introduce Reprojection Error (RPF) Duan et al. 2025) and Revisit Error (RVE) (Xiao al., 2025). Reprojection Error (RPE) quantitatively mea- sures multi-view geometric consistency by calculating the average reprojection discrepancy between projected and observed pixel locations across frames. Revisit Error (RVE) assesses long-range tem- poral consistency by examining discrepancies between initial and revisited frames under complete camera rotation. provide more details of these metries in the Appendix (Sec. C.4).
5.1 MAIN RESULTS
This section presents the main experimental results, comparing method against state-of-the-ane approaches across different tasks. The evaluation results demonstrate the effectiveness and general- ization ability of our method in both short- and long-term teem video generation.

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DFoT
VideoREPA
REPA
GF (ours)
360
Figure 2: Qualitative comparison of camera view-conditioned video generation under full- circle rotation. Videos are generated from a single frame and per-frame camera poses simulating full 360 rotation. Our method (GF) is compared with DFoT (Song et al., (025). VideoREPA (Zhang) et al., 2025c), and REPA (Yu et al.) 2024a). The results demonstrate that the baseline methods fail to maintain temporal consistency, while our proposed GF consistently revisits the starting viewpoint.
Camera view-conditioned Video Generation. We conduct comprehensive evaluation of GF on the 10KK (Zhou al., 2018) dataset, comparing against state-of saate-of-the-ar tithhe-att baselines. We report results for both short-term (16-Frame) and long-term (256-Frame) video generation in Tab. I As shown in Tab. [ our method consistently outperforms all baselines across multiple evaluation metries, including FVD, LPIPS, SSIM, and PSNR, in both the short-term and long-term genera- tion settings. These results highlight the effectiveness of GF in enhancing visual fidelity, temporal stability, and 3D spatial consistency, thereby enabling more realistic and coherent world modeling.
Action-conditioned Video Generation. To demonstrate the generality our method, we apply GF to Next-Frame Diffuusion (Cheng et al. 2025) model. As shown in Tab. 5 the model achieves lower FVD score which indicates GF can be seamlessly integrated into video diffusion models and leads to measurable gains. Note that, there exists large data distribution gap between real world and Minecraft. This results demonstrate that GF generalize well on out-of-domain distribution.
5.2 QUALITATIVE RESULTS
Fig. presents qualitative comparisons the RealEstate 10K dataset. Each video is generated from single input frame along with per-frame camera poses simulating full 360 rotation. We compare GF against three strong baselines: DFoT (Song et al. 2025). REPA (Yu et al. 2024a), and VideoREPA (Zhang et al., 2025c). As shown in Fig. our method reconstructs the initial frame when the camera completes rotation, while producing reasonable and realistie intermediate views. In contrast, the baseline methods fail to maintain temporal coherence and scene consistency, resulting in implausible intermediate frames and unable to revisit the starting viewpoint. These results highlight the superior long-term 3D consistency and scene understanding of our approach.
5.3 ABLATION STUDIES
We provide a series of ablation studies to validate the design of GF.
Which Representation Should be Aligned? To validate the effectiveness of geometric represen- tation, we compare two target representations in GF; VGGT (Wang et al. (025b), trained on 3D datasets with strong geometric priors, and DINOv2 (Qqua et al. 2023), trained on 2D images focusing on semantic features. As shown in Tab. 2 aligning with VGGT consistently outperforms DINOv2 on both long-term and short-term generation tasks, highlighting the advantage geometric alignment over semantic supervision.

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Table 2: Ablation study on target represen- tation. We compare the effect of aligning the diffusion model with different target representa- tions: DINOv2 (semantic). VGGT (geometric), and their combination. The joint use both representation achieves the best FVD.
Table 3: Ablation study on alignment loss. Angular and Scale Alignment losses are evalu- ated for long-term video generation, with MSE as naive baseline of aligning both angular and scale information. The combination of Angular and Scale Alignment yields the best results.
Target Representation
FVD-256
Alignment Loss
FVD-256
Baseline
364
Baseline
364.0
DINOv2 Only
297
Angular
253.0
VGGT Only
243
Angular Scale
243.0
VGGT + DINOv2
237
MSE
1648.0
Ablation study on explicit and im- plicit geometry information. We compare ex- plicit geometry condition with internal align- ment (ours).
Table 4:
Table 5: Evaluation on action-conditioned video generation in Minecraft. FVD results of NFD before and after applying Geometry Forc- ing (GF) on 16- (6-Frame generation show clear im- provement.
Method
FVD-2566
Method
FVD-161
Baseline
364
Rendered Image Injection
280
NFD
216
Latent Feature Injection
275
NED GF
205
Geometry Forcing (ours)
243
To further explore their complementarity, we combine VGGT and DINOv2 features as joint su- pervision targets. Results in Tab. [ show that integrating geometric and semantic signals leads to additional gains, suggesting that the two types of representations are orthogonal and can enhance each other when used together. However, diffusion model and the dynamic 3D structure of the real world, we only use VGGT features in further experiments.
mainly focus on bridging the gap between the video
Alignment Loss. GF consists of two alignment objectives: Angular Alignment and Scale Align- ment. To validate their effectiveness, compare three alignment loss types: (1) Angular Align- ment alone (Sec. (42).. (2) Angular Alignment with Scale Alignment (Sec. 4.2) and (3) MSE loss between VGGT and diffusion features. As shown in Tab. 3 the combination of Angular Alignment and Scale Alignment achieves best performance, indicating the benefit of aligning both angular and scale-related information. Although direct mean squared error (MSE) also supervises magnitudes, the change of feature scale of the diffusion model may cause collapse in the following layers. These results highlight that neither Angular Alignment nor Scale Alignment alone sufficient. Explicitly or Implicitly Integrate Geometry Information into Video Diffusion Models?
To assess the benefit of internalizing geometric representations, we compare two ways of incorpo- rating geometry into the video diffusion model: internal alignment via GF and external guidance via a ControlNet (Zhang et al.) 2023). For external guidance, we test two settings: (1) injecting VGGT intermediate features into a ControlNet attached DFoT, and (2) reconstructing the 3D scene, ren- dering it into 2D images, and injecting the rendered images geometric conditions. As shown in Tab. using the same VGGT features, GF outperforms explicit feature injection, and rendered- image conditioning also lags behind GF. These results show that while explicit geometric cues helpful, internal alignment through GF provides consistently stronger supervision. By aligning inter- nal features with geometric representations, GF enables deeper geometric understanding and yields better performance in terms perceptual quality and structural consistency. Full evaluation results is listed here Tab.
Which Layer Should be Aligned? As shown in Fig. 3 we also explore applying alignment at
different layers of the video diffusion model (Song et al. (025), which uses a 7-layer U-VIT (Bao cet al. 2023) backbone (3 downsampling layers, bottleneck layer, upsampling layers). Aligning layer yields the best FVD-256 score while preserving FVD-16 performance.
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800
370
364
355
700
320
311
316
303
270
291
252
FVD
253
220
230
221
219
220
170
200
196
204
400
GF-256
16
Baseline
GF
120
300
1
3
5
7
Alignment Depth
Frame Index
Figure 3: Ablation study on alignment depth. We present FVD-256 and FVD-16 results for different alignment layers of diffu- sion model which suggest mid-level feature is most effective to improve video quality.
Figure 4: Exposure bias analysis. This fig- ure shows the trend of FVD scores during long-term video generation. Compared to the baseline, GF results significantly lower FVD after 100 frames.
Table 6: User study. Average scores on Camera Following, Object Consistency, and Scene Conti- nuity. Each user has to rate each dimension on a scale of 5. Higher values indicate better quality.
Method DFoT
Camera Following
Object Consistency
Scene Continuity
3.56
2.73
2.74
REPA
3.82
3.55
3.66
VideoREPA
3.31
3.05
2.82
Geometry Forcing
4.40
4.44
4.52
Mitigating Exposure Bias in Autoregressive Video Diffusion Model via Geometry Forcing. Exposure bias is long-sto long-standing challenge in autoregressive video generation (Chen al. 2024a Song et al. 2025 Sun et al. 2025 Cheng et al. 2025 Huang et al. (025b). While previous methods attempted address it through memory mechanisms context guidance, GF offers an orthogonal solution. As shown in Fig. GF mitigates long-term drift and reduces the accumulation er- ror during generation significantly by aligning 3D geometric representation. These results validate integrating 3D representation enables more reliable and coherent long-term video synthesis.
5.4
USER STUDY
While Reprojection Error (RPE) and Revisit Error (RVE) provide useful signals for measuring 3D consistency, they only capture specific geometric aspects and may miss perceptual artifacts unre- alistic dynamics that humans can easily notice. Additionally, we conduct a user study focusing on three aspects of 3D consistency. 1) Camera Following: Whether the camera in the video moves smoothly and accurately follows the given pose trajectory. 2) Object Consistency: Whether objects remain consistent in shape, appearance, and position across frames. 3) Scene Continuity: Whether the generated parts of the scene beyond the context frames remain coherent and reasonable.
We compare GF with DFoT (Song al. 2025). REPA YYu al. 2024a). and VideoREPA (Zhang et al. 2025c). As shown in Tab. 66, GF consistently outperforms all baselines across the three aspects of 3D consistency, demonstrating its effectiveness in producing geometrically coherent videos.
6 CONCLUSION
This paper introduces Geometry Forcing (GF), a simple yet effective framework that enhances the geometric consistency of autoregressive video diffusion models by aligning their internal represen- tations with geometry-aware features. Motivated by the observation that video diffusion models trained on raw pixel data often fail capture meaningful 3D structure, alignment objectives (Angular Alignment and Scale Alignment) guide the latent feature align with 3D-aware freature from geometric foundation model. Empirical results on both camera-conditioned
method proposes two

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and action-conditioned video generation benchmarks demonstrate that GF significantly improves visual quality and 3D consistency, yielding lower FVD scores and more stable scene dynamics.
Limitations. The primary limitation of this work lies in its scale. While GF consistently improves geometric consistency and visual quality, its full potential remains unexplored under large-scale training. In particular, we have not yet investigated its effectiveness when applied larger models and more extensive video datasets, which may further amplify its benefits.
Future Work. Future directions include scaling GF on larger datasets to build 3D-consistent world simulators, and applications for long video generation by treating 3D representation as memory.
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REPRODUCIBILITY
We provide comprehensive implementation details, including model architectures, training configu- rations, and data preprocessing procedures, in Appendix Co ensure reproducibility.
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APPENDIX FOR ICLR26 SUBMISSION Geometry Forcing: MARRYING VIDEO DIFFUSION AND 3D REPRESENTATION FOR CONSISTENT WORLD MODELING
Declaration of LLM usage
Limitations
Implementation Details
Dataset
Training
Inference
Metries
C,5
3D Reconstruction from Diffusion Features
D Supplementary Experiments
D.I
Ablation on Teacher Model
D.2
Explicit Geometry Control
D.3
Alignment Context Length
D.4
Multiple Layer Alignment
D.5
Text-to-video generation
Discussion
Computational Efficiency
Analysis of Geometric and Semantic Representations
3D Consistency and Exposure Bias Mitigation
E,4
Failure Case Analysis
Supplementary Visualizations
A DECLARATION OF LLM USAGE
We used large language models (LLMs) to aid or polish writing. Details are described the paper. The use is limited to language editing (grammar, spelling, and word choice), code formatting (e.g.. adding comments to the code). All scientific ideas, analysis, and conclusions were conceived, val- idated, and interpreted independently by the authors. We gratefully acknowledge the assistance of large language models in our work.
B LIMITATIONS
Our method's reliance on VGGT (trained mainly on static scenes) constrains performance in dy- namic environments. Generalization significant motion scenarios requires further research.

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C IMPLEMENTATION DETAILS
C.I DATASET
RealEstateI0K (Zhou al. 2018). This dataset contains camera poses for 10 million video frames, suitable for evaluating 3D consistency and camera navigation in generated vidcos. We use resolution of 256 256 pixels.
Minecraft (Baker et al. 2022). This game dataset includes action annotations, enabling evalua- tion of video generation dynamic environments with camera motion.
Alignment Projection To maximize geometric information retention, we aggregate features from all transformer blocks the VGGT backbone as alignment targets. For computational efficiency, apply bilinear interpolation reduce the spatial dimensions from the original resolution manageable size of 512x512 tokens.
The alignment is performed using Conv3D-based projector that operates on the latent dimensions. To accommodate multi-layer and multi-target alignment scenarios, we initialize independent pro- jectors for each feature layer and target representation. This design ensures effective dimensional compatibility between the U-ViT feature space and the target geometric representations while main- taining computational efficiency.
C.2 TRAINING
Model Architecture. We adopt a U-ViT Bao et al. (2023) backbone for video generation, with geometric feature alignment integrated the third transformer block.
Training Data. The model is trained on 10,000 video clips sampled from the RealEstate100 train- ing dataset, each comprising 16 consecutive frames.
Training Protocol. Training proceeds for 2 epochs using learning rate of 8 x 10-6 and global batch size of 40. The geometric alignment loss is combined with the standard diffusion training objective.
C.3 INFERENCE
A key advantage of Geometry Forcing is its inference-time efficiency which introduces no compu- tational overhead during sampling. We demonstrate results using DDIM sampler with 50 steps, though the approach is compatible with any standard diffusion sampling algorithm.
C.4 METRICS
In this section,we we introduce the detailed implementation of Reprojection Error (RPE) and Revisit Error (RVE).
Reprojection Error. Reprojection error (RPE) is a widely used metric in visual SLAM to evaluate multi-view geometric consistency. Following Duan al. (2025), utilize DROID- SLAM (Teed & Deng 2021) to reconstruct scene. Specifically, DROID-SLAM first extracts corre- sponding features across frames and then refines camera poses (G,) and per-pixel depth estimates (d,) through its differentiable Dense Bundle Adjustment (DBA) optimization, enforcing optical flow The reprojection error then computed by
constraints and achieving robust cenneuure-frommoooiee measuring the average Euclidean distance between the projected and observed pixel locations of co-visible 3D points across multiple frames. Formally, RPE defined as:
RE
1
Σ (i.))EV
P
IT(P;)))
VI
where V/ denotes the set of valid feature correspondences, pij is the observed pixel location in gen- erated video frames, Pij represents the corresponding reconstructed 3D point derived from refined
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Table 7: Ablation study on teacher model Our method (Geometry Forcing) is compatitable with different teachmodel including VGGT and Pi3bold values denote the best, and Underlined values indicate the second best. indicates the method is conditioned on the first frame only.
Method
Frames
FVDL
LPIPS
SSIM
PSNRT
RPF4
RVEL
DFoT (Song al. 2025
256
364
0.55
0.36
11.40
0.3575
297
Pi3 Wang et al. (2025
256
309
0.53
0.38
11.53
0.x
X
Geometry Forcing (VGGT)
256
243
0.51
0.38
11.87
0.3337
272
depths and camera poses, and II denotes the camera projection function. Lower RPE values indi- cate better 3D alignment, reduced spatial artifacts, and enhanced spatio-temporal stability, thereby effectively reflecting the overall geometric coherence and consistency of the generated videos.
Revisit Error. Revisit Error evaluates long-rongee temporal consistency under full camera rotation, inspired by the setup proposed WorldMem (Xiao et al. 2025). For each of 100 randomly sampled RealESstte 10K video clips, extract the first frame and initial camera pose. A camera trajectory of 256 frames is then constructed by rotating the initial camera pose around the Y-axis. We assess revisit consistency by comparing the first and final frame using reconstruction FID (rFID) (Heusel et al. (017). Larger discrepancies indicate greater geometric or appearance drift, suggesting weaker long-teri 3D consistency.
C.5 3D RECONSTRUCTION FROM DIFFUSION FEATURES
In this section, we provide detailed overview of the 3D reconstruction process illustrated in Fig. Cc).
Reconstruction using Geometry Forcing Features. We extract features from the Geometry Forc- ing (GF) model and pass them through the depth prediction head of VGGT to obtain the predicted depth map.
Reconstruction using Diffusion Features. Motivated by linear probing experiments, we in- vestigate the 3D reconstruction capability of intermediate features extracted from DFoT (Song et al. freeze the pretrained DFoT backbone and train DPT head (Ranftl al., 2021 to regress depth maps from its intermediate representations. The target depth maps are pro- vided by the VGGT model (Wang et al., 2025b), serving ground-truth supervision. The DPT head adopts the same architecture as the depth prediction module used in VGGT but is trained from scratch. We optimize the DPT head for 2500 steps using a learning rate of x 10-4 and batch size of 4.
2025 Specifically,
D SUPPLEMENTARY EXPERIMENTS
D.1 ABLATION ON TEACHER MODEL
Geometry Forcing don't depend on specific 3D foundation model but still requires the 3D foundation to be feed-forward and support multiple images input which is required by online training. We conduct Geometry Forcing algorithm on P; model and also achieves significant improvement on video generation as shown in Tab.
D.2 EXPLICIT GEOMETRY CONTROL
We provide full evaluation comparison between explicit control and ours Geometry forcing in Tab.
D.3 ALIGNMENT CONTEXT LENGTH
Our Geometry Forcing input 16 frames into VGGT model to extract the latent representation and then align first 16 frame during training. We provide the ablation results different alignment
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Published as a conference paper at ICLR 2026
Table Ablation study on explicit and implicit geometry information. Our method (Geometry Forcing) achieves the best performance across all metrics on the RealEstate 10K dataset for long- term (256-Frame) video generation. bold values denote the best, and Underlined values indicate the second best. indicates the method is conditioned on the first frame only.
Method
Frames
FVD.
LPIPS1
SSIM
PSNR1
RPEL
RVEJ
DFoT (Song al. 2025
256
364
0.55
0.36
11.40
0.3575
297
Render Image Injection
256
280
0.52
0.37
11.99
Latent Feature Injection
256
364
0.55
X
0.x
Geometry Forcing (ours)
256
243
0.51
0.38
11.87
0.3337
272
Table 9: Ablation study on GF alignment context length. Geomtry Forcing-n indicates n frames
used to extract VGGT feature during training. The results is evaluated the RealEstate 10K dataset for long-term (256-Frame) video generation. bold values denote the best, and Underlined values indicate the second best. indicates the method is conditioned the first frame only.
Method
Frames
FVDJ
LPIPSI
SSIM
PSNR1
RPET
RVEJ
DFoT (Song al. 2025
256
364
0.55
0.36
11.40
0.3575
297
Geometry Forcing-4
256
261
0.51
0.38
12.21
Geometry Forcing-8
256
257
0.50
X
X
0.38
12.27
Geometry Forcing- 16 (default)
256
243
0.51
0.38
11.87
0.3337
272
context length in Tab. 9 The results indicates that the when the alignment context length is longer, the 3D information is more complete, thus lead to better results.
D.4 MULTIPLE LAYER ALIGNMENT
Since the large number of potential combination of layers selected for alignment, we provide the result on align with last layers of our diffusion model in Tab. 10 However, increasing number of layers to align doesn't 't lead to better performance.
D.5 TEXT-TO-VIDES GENERATION
We extend our Geometry Forcing method to general text-to-video generation tasks. Our model is trained on 2K videos from the Wang et al. (2025a), which provides videos with detailed scene and camera descriptions. Experimental results demonstrate that our approach achieves improvements across multiple evaluation dimensions, including visual aesthetics, motion smoothness, and motion quality, as detailed in Table 11 These results indicate that Geometry Forcing extend effectively to dynamic text-to-video training, even though VGGT itself is trained on static scenes.
E DISCUSSION
E. COMPUTATIONAL EFFICIENCY
We perform a detailed profiling of our method on a NVIDIA A800 GPU and report both the ex- ecution time and Hloating-poin operations (FLOPs) for different components of our model during the training stage in Table 12 The VGGT Feature Alignment contributes an additional 52.5% in execution time and 60.4% total FLOPPs Although this alignment process increases the per-step computation compared to the base diffusion model. it significantly accelerates convergence, thereby reducing the overall training duration. For fine-tuning, our method requires only a few thousand steps and completes within hours, yielding substantial efficiency gains over full pre- mre-trinnnng. Ad- ditionally, during inference, our method does not introduce any additional computational cost com- pared to other methods that explicit implicit memory.
We also provided feature extraction time VGGT model Fig 5 extraction time increases from 0. 0.8s when the input view from T to 12.
The result shows that the

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Published as a conference paper at ICLR 2026
Table 10: Alignment on Multiple Layers. Comparison of aligning VGGT features the middle layer vs. the last three layers of the diffusion model using Geometry Forcing,
Method
Frames
FVDJ
LPIPSL
SSIM
PSNR1
RPEL
RVEJ
DFuT (Song al. 2025
256
364
0.55
0.36
11.40
0.3575
297
Geometry Forcing (Last Layers)
256
309
0.53
0.38
11.53
0.xxx
XXX
Geometry Forcing (Mid)
256
243
0.51
11.87
0.3337
272
Table 11:
Evaluation on text-conditioned video generation. FVD results of Wan2.1 1.3B before and after applying Geometry Forcing (GF) on 81-frame generation show clear improvement.
Method
Aesthetic Quality
Imaging Quality?
Motion Smoothness?
Wan2,1
0.58
0.56
0.98
Wan2.1 GF
0.59
0.59
0.99
E.2 ANALYSIS GEOMETRIC AND SEMANTIC REPRESENTATIONS
We analyze the roles of geometric and semantic representation alignment in video generation. First, these representations exhibit considerable overlap rather than orthogonality. Semantic representa- tions like DINOv2 (Oquab et al. 2023) demonstrate zero-shot depth estimation capabilities (see Section 7.5 and Figure the original paper), indicating inherent geometric understanding. Con- versely, geometric representations such as VGGT utilize DINOv2 features inputs, thereby encod- ing semantic information.
Second, experimental results in Table and Table 2 show that VGGT alignment primarily enhances 3D consistency, while DINOv2 alignment improves visual quality. The combination of both repre- sentations achieves superior performance compared to either individual approach.
Finally, the distinct contributions of each representation can be characterized as follows: semantic alignment enhances object realism and visual details, whereas geometric alignment ensures struc- tural consistency and shape coherence throughout the generated video sequences.
E.3
3D CONSISTENCY AND EXPOSURE BIAS MITIGATION
As shown in Figure the FVD metric increases at a slower rate when Geometry Forcing is em- ployed, indicating efrective mitigation of exposure bias in long-term video generation. The under- lying mechanism can understood through the inherent stability of 3D scenes: while the number of generated frames increases, the underlying scene geometry remains same. Geometry Forcing en- ables the model internalize this geometric consistency, thereby reducing error accumulation when regenerating frames from previously encountered viewpoints.
E.4 FAILURE CASE ANALYSIS
Although our methods significantly improve visual quality and geometric consistency in video gen- eration, they still struggle certain complex scenarios. As shown Fig. the transparent, reflective glass table intermittently disappears and reappears across frames, indicating that the model still has difficulty handling reflective materials.
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Published as a conference paper at ICLR 2026
Feature Extract Time vs. VGGT Input View Number
Feature Extract Time
0.8
0.6
Time (5)
0.5
0.3
0.2
10
12
13
14
15
16
VGGT Input View Number
Figure 5: VGGT Feature Extraction Time. The feature extraction time of VGGT model increases along the number of inout view.
Figure 6: Failure Case Analysis. The transparent, reflective glass table intermittently disappears and reappears across frames, indicating that the model still has difficulty handling reflective materi- als. The red box indicates when the table disappears.

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Published as a conference paper at ICLR 2026
Table 12: Training Stage Profiling. We report the execution time and Hloating-point operations
(FLOPs) for different components during a training step.
Time
FLOPs
Pipeline Stage
Value (s)
Percentage (%)
Value (T)
Percentage (%)
Forward (Frozen)
VGGT Encoding
0.853
53.4%
93.3
60.4%
Forward (Leornable)
Projector
0.017
1.1%
0.1
0.1%
Diflusion Backbone
0.220
13.8 %
17.7
11.5%
Backward (Learnable)
Projector Diffusion Backbone
0.506
31.7%
43.5
28.1 %
Total per Step
1.597
100.0%
154.6
100.0
SUPPLEMENTARY VISUALIZATIONS
In order to better understand the geometry influences, we provide comprehensive visual results.
VileoRERA
REPA
T=0
T=255
T=0
T=255
Figure 7: Qualitative comparisons camera--cnditioneed video generation. All the videos generated given first frame and per-frame camera pose. We comprehensively compare GF (ours) with DFoT (Song et al. 2025), VideoREPA (Zhang al., 2025c), REPA (Yu al.) 2024a). The results demonstrate consistency long-term video generation both inside (left) and outside (right) scenes.
Fig. presents qualitative comparisons the RealEstate10k dataset. Given the first frame and per-frame camera trajectory as input, we compare our proposed GF method with three strong baselines: DFo' (Song et al. 2025). REPA (Yu 2024a), and VideoREPA (Zhang et al. 2025c).
As shown in Fig. our method generates visually coherent and geometrically consistent videos when context is limited. In particular, GF better preserves object
long time horizons shapes and scene layouts that is visible in context, while generating reasonable scenes not seen in the context. In contrast, baseline models often exhibit drift, shape distortion, abrupt transitions. These results highlight the effectiveness of internalizing geometric priors to enhance spatial and temporal consistency in video generation.
To further assess the impact of the proposed scale align- ment loss, we conduct qualitative comparisons between models trained with and without this com- ponent (Fig. g While angular alignment alone helps maintain basic geometric coherence, the lack of scale supervision often leads to inconsistent camera motion, manifesting as unstable perspective changes unnatural object scaling. By introducing the seale alignment loss, method produces
Qualitative Ablation on Alignment loss.

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Published as a conference paper at ICLR 2026
noticeably smoother viewpoint transitions and more reliable camera-following behavior, demon- strating its effectiveness in stabilizing nulti-frame geometry.
W SA
SA
W VS
Figure 8: Qualitative comparison of the Alignment Loss. "w/ SA" denotes models trained with both angular alignment and scale alignment losses, while "w/o SA" refers to models trained using only angular alignment. Incorporating scale alignment enables the model to generate videos with more stable and realistic camera-following behavior.
8


Reviews:
[{"review_id": "uQTGwSpNVB", "reviewer": "Reviewer_AqJj", "summary": "This work introduces Geometry Forcing, a method designed to help video diffusion models better capture the inherent 3D structure of real-world scenes. While standard video diffusion models trained on raw 2D video data often lack geometric understanding, Geometry Forcing addresses this by aligning the model’s internal representations with features from a geometric foundation model. It employs two key alignment strategies: Angular Alignment, which enforces directional consistency through cosine similarity, and Scale Alignment, which preserves scale information via feature regression. Experiments on camera view–conditioned and action-conditioned video generation show that Geometry Forcing significantly enhances both visual quality and 3D consistency, outperforming existing baselines.", "questions": "1.The diffusion model and VGGT have different learning objectives — one is progressive (learning noise or velocity), while the other is result-oriented. How can the proposed alignment between these fundamentally different targets work effectively?\n\n\n2.The motivations and mechanisms of Angular Alignment and Scale Alignment are unclear. How exactly do these objectives influence the final video generation quality?\n\n\n3.What specific model does the “U-ViT backbone for video generation” refer to? Could the authors provide more details or a proper citation?\n\n4. Could the authors include explicit geometry-based video generation methods as additional baselines to make the comparison more convincing?\n\n\n5.The paper lacks qualitative ablation studies showing the individual effects of Angular and Scale Alignment. Can the authors provide such qualitative analyses?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is clearly written and well-organized.\n2. The core idea is simple yet effective — aligning the internal representations of video diffusion models with features from a geometric foundation model.\n3. Experimental results demonstrate strong performance, showing notable improvements in geometric consistency and long-term temporal coherence compared to baseline methods.", "weaknesses": "The training objectives of the diffusion model and the VGGT differ fundamentally. The diffusion model is designed to learn noise or velocity in a progressive manner—its target lies in the intermediate denoising process rather than the final outcome. In contrast, VGGT is result-oriented, directly learning to predict the final geometry. Although the experimental results appear promising, theoretically the learning targets are not of the same nature and may even be somewhat conflicting. It remains unclear how the proposed alignment between these two objectives effectively works in practice. Could the authors provide more theoretical or empirical justification for this compatibility?\nThe motivations and formulations of Angular Alignment and Scale Alignment are insufficiently explained. The directional correspondence between the hidden states of the diffusion model and the geometric features, as well as the scale differences across models, are both vague. It would be helpful to clarify how these factors influence the final generation quality.\n\nThe base model description is also unclear. The paper mentions “a U-ViT backbone for video generation,” but does not specify which model this refers to or provide an appropriate citation.\n\nIncluding explicit geometry-based video generation methods as comparison baselines would strengthen the evaluation and provide more persuasive evidence of the proposed method’s effectiveness.\n\nAdditionally, there are citation errors, such as the one noted around line 290.\n\nFinally, the paper lacks qualitative ablation studies that isolate the effects of Angular Alignment and Scale Alignment. Without such analyses, it is difficult to assess the actual contributions of each component to the overall improvement.", "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": "SkCXjLQrOF", "reviewer": "Reviewer_PFvJ", "summary": "This paper adapts the REPresentation Alignment (REPA) [1] into 3D domain. Specifically, it employs VGGT [2] as a 3D foundation model and aligns the intermediate features of a video diffusion model with features extracted from layers of VGGT. The authors propose two alignment objectives for this purpose: an angular alignment objective, which enforces cosine similarity between feature maps, and a scale alignment objective, which directly supervises the rescaled features of the diffusion model. Experiments demonstrates that combining these two objectives enhances the video diffusion model's 3D understanding, thereby improving geometric consistency in the generated results.\n\n[1] Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think\n\n[2] VGGT: Visual Geometry Grounded Transformer", "questions": "*  The matching process of VGGT seems computationally expensive, especially as the number of views increases, potentially making the training alignment prohibitively slow. To clarify this, could the authors report the concrete training time?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "* The core concept of aligning intermediate features of a generative model with those of a foundation model is a promising research direction that has shown success in other fields.\n* The proposed method is simple yet effective. The experiments demonstrate improvements over the baseline models.\n* The paper is well-written and easy to understand.", "weaknesses": "* My primary concern is the paper's limited novelty. The proposed method appears to be a straightforward adaptation of the 2D alignment technique from REPA. The core contribution seems to be replacing the DINO model (used in 2D REPA) with VGGT for the 3D case. As such, the work feels incremental and offers limited new conceptual insights.\n\n* The method's applicability appears to be limited to static scenes, but this is not explicitly stated. The authors should clearly acknowledge that the current approach does not handle dynamic scenes or significant camera motion, which restricts its use to a narrow set of conditions. \n\n*  The performance of the VGGT feature extraction likely depends on the number of views used. I would encourage the authors to provide an ablation study demonstrating how model performance varies with different numbers of views adopted during training time.", "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": "s3JlhoV03n", "reviewer": "Reviewer_J25o", "summary": "The paper introduces Geometry Forcing (GF) that enhances the geometric consistency of video diffusion models by aligning their internal representations with those of a 3D foundation model (VGGT). GF introduces Angular Alignment and Scale Alignment. The method is integrated into standard autoregressive video diffusion training without requiring explicit 3D supervision. Experiments on RealEstate10K and Minecraft benchmarks show that GF improves Fréchet Video Distance (FVD), SSIM, and 3D consistency metrics (RPE/RVE) compared to state-of-the-art baselines such as DFoT, REPA, and VideoREPA. Ablation studies confirm the complementary role of Angular and Scale alignment, and user studies indicate perceptually improved scene consistency.", "questions": "1. Generality of the 3D teacher. How sensitive is GF to the choice of 3D foundation model? Would a weaker teacher (e.g., DUST3R) still yield benefits, or is VGGT’s strong geometry essential?\n\n2. Does alignment at all layers or at multiple scales (spatial or temporal) offer further improvement, beyond the mid-level layer shown to be best?\n\n3. What is the added training cost (e.g., % increase in FLOPs or wall-clock time) from Geometry Forcing, given the need to compute VGGT features?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is very well written, with clear figures illustrating motivation and results.\n\n2. The paper proposes a new concept of “geometry forcing”, transferring geometric awareness into video diffusion models without requiring 3D ground-truth supervision. The dual-objective design (Angular and Scale alignment) is interesting, addressing optimization stability in cross-domain feature matching.\n\n3. Experiments are extensive. Quantitative: Benchmarks include both 16- and 256-frame video generation, using perceptual and geometric metrics. Qualitative: Visual comparisons (360° rotations) convincingly show consistent viewpoint revisiting.", "weaknesses": "1. Scale of experiments is modest (16–256 frames, 256×256 resolution). The authors acknowledge this but it limits claims of scalability.\n\n2. While the ablations cover alignment types and layer depths, computational cost (training overhead, memory footprint) is missing. Geometry alignment likely adds feature extraction and projection costs that may be nontrivial.\n\n3. The method relies heavily on VGGT as a teacher. It’s unclear whether GF’s success depends on the specific 3D foundation model or generalizes to others (e.g., DUST3R, FLARE).\n\n4. There is limited discussion of failure cases (e.g., ambiguous depth, reflective surfaces).", "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": "hDimBEMAcJ", "reviewer": "Reviewer_qFpu", "summary": "This paper proposes Geometry Forcing, a REPA-style feature alignment method to enhance the 3D consistency of existing video diffusion models (VDMs). Authors first observe that existing VDMs cannot readout consistent 3D representations (i.e., point maps) from their diffusion features. To address this, prior works either jointly predict the 3D modality, or leverage a structured 3D representation as guidance. Geometry Forcing instead proposes to align the internal features of the VDM with the representation of a 3D foundation model, VGGT. Fine-tuning pre-trained VDMs with such auxiliary loss significantly improves the temporal consistency of the generated videos, demonstrated by both quantitative and user study results.", "questions": "Besides the questions in Weaknesses, here are some minor questions:\n1. Can you explain why VideoREPA achieves a significantly better RVE than Geometry Forcing, while being much worse in other metrics?\n2. How do you implement VideoREPA? Is it doing REPA alignment loss with per-frame DINOv2 features? Then why is its result much worse than the \"DINOv2 Only\" entry in Tab.2?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The idea is simple and intuitive. Adding a REPA loss with VGGT does not introduce any sophisticated architecture design, yet it is able to bake the 3D information into the model.\n- The ablation study is very comprehensive, which clearly shows the effectiveness of each module. \n- The experimental results seem strong. Geometry Forcing outperforms baselines in most of the metrics.", "weaknesses": "1. As the author pointed out in the Limitation, VGGT is only trained on static scenes, and thus cannot be used to supervise VDM training on dynamic videos. Can authors discuss more on how to extend Geometry Forcing to dynamic videos as required by general text-to-video training?\n2. Have the authors tried other 3D foundation models? For example, MonST3R [1] and CUT3R [2] as they can handle dynamic videos.\n3. Have you tried training a video diffusion model from scratch? REPA shows that using representation alignment loss can greatly speed up convergence. I'm curious if applying both DINO and VGGT loss can further accelerate this. I understand that the computation cost is high, so showing some early training loss curve (with vs without Geometry Forcing loss) is enough (or describe it, if figures are not allowed in rebuttal).\n4. I'd like to see a comparison with a baseline that uses explicit 3D memory, e.g., GEN3C [3] that uses reprojected point clouds as additional conditioning for the VDM. You do not need to apply all its components. I think you can do this:\n- To generate frame `n`, run VGGT on frame `1, 2, ..., n-1` to reconstruct a point cloud of the scene, then render it to 2D images, and condition the VDM on it. This can also work in an autoregressive way.\n\n[1] Zhang, Junyi, et al. \"Monst3r: A simple approach for estimating geometry in the presence of motion.\" arXiv preprint arXiv:2410.03825 (2024).\n\n[2] Wang, Qianqian, et al. \"Continuous 3d perception model with persistent state.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[3] Ren, Xuanchi, et al. \"Gen3c: 3d-informed world-consistent video generation with precise camera control.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.", "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)