OpenNovelty.org

Physics-Guided Motion Loss for Video Generation Model

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.3Download Report PDF
Video generationDiffusion model
Abstract:

Current video diffusion models generate visually compelling content but often violate basic laws of physics, producing subtle artifacts like rubber-sheet deformations and inconsistent object motion. We introduce a frequency-domain physics prior that improves motion plausibility without modifying model architectures. Our method decomposes common rigid motions (translation, rotation, scaling) into lightweight spectral losses, requiring only 2.7% of frequency coefficients while preserving 97%+ of spectral energy. Applied to Open-Sora, MVDIT, and Hunyuan, our approach improves both motion accuracy and action recognition by ~11% on average on OpenVID-1M (relative), while maintaining visual quality. User studies show 74--83% preference for our physics-enhanced videos. It also reduces warping error by 22--37% (depending on the backbone) and improves temporal consistency scores. These results indicate that simple, global spectral cues are an effective drop-in regularizer for physically plausible motion in video diffusion.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a frequency-domain physics prior that decomposes rigid motions into spectral losses, requiring only 2.7% of frequency coefficients while preserving 97%+ spectral energy. It resides in the Physics-Based Loss Functions leaf, which contains four papers including this one. This leaf sits within the broader Physics-Guided Motion Regularization branch, indicating a moderately populated research direction focused on explicit physics constraints. The sibling papers in this leaf explore related physics-based loss strategies, suggesting the area is active but not overcrowded, with room for distinct technical approaches to motion plausibility.

The taxonomy reveals neighboring research directions that contextualize this work. Physics-Aware Inference Guidance (four papers) explores training-free guidance methods, while Physics Data Fine-Tuning (three papers) uses specialized datasets to learn physical behaviors. The Motion Prior Learning and Alignment branch (nine papers across three leaves) takes a complementary approach by learning motion patterns from pretrained encoders rather than enforcing explicit physics constraints. The paper's frequency-domain approach bridges these paradigms: it enforces physics explicitly through spectral losses but does so in a lightweight, drop-in manner that resembles the flexibility of inference-time guidance methods.

Among 19 candidates examined across three contributions, none clearly refute the core claims. The frequency-domain physics prior framework examined 9 candidates with 0 refutable matches, suggesting limited direct overlap in the spectral decomposition approach. The differentiable motion-aware regularizer examined only 1 candidate, indicating sparse prior work on adaptive weighting mechanisms for physics losses. The drop-in regularizer contribution examined 9 candidates with 0 refutable matches, though the limited search scope means comprehensive coverage of all physics-based regularization methods cannot be guaranteed. The statistics suggest the specific combination of frequency-domain analysis and lightweight regularization appears relatively unexplored within the examined literature.

Based on the top-19 semantic matches examined, the work appears to occupy a distinct technical niche within physics-guided video generation. The frequency-domain formulation and minimal coefficient requirement differentiate it from sibling papers in the same taxonomy leaf, though the limited search scope means potentially relevant work in signal processing or classical motion analysis may not have been captured. The analysis covers physics-focused video diffusion methods but does not exhaustively survey broader frequency-domain techniques in computer vision or graphics.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
19
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Improving physical motion plausibility in video diffusion models. The field has organized itself around several complementary strategies for making generated videos obey real-world physics and motion dynamics. Physics-Guided Motion Regularization introduces explicit constraints or loss functions that penalize physically implausible trajectories, often drawing on classical mechanics or learned physical priors. Motion Prior Learning and Alignment focuses on extracting motion representations from real data—such as optical flow, trajectories, or learned embeddings—and aligning generated content with these priors. Explicit Motion Conditioning provides direct control signals (e.g., trajectories, poses, or flow maps) to guide the diffusion process, while Temporal Consistency and Architecture addresses architectural innovations that enforce coherent frame-to-frame transitions. Domain-Specific Video Generation tailors models to particular scenarios like human motion or object interactions, and Camera and Viewpoint Control manages 3D geometry and camera dynamics to ensure spatial coherence. Representative works such as Mofusion[1] and DynamiCrafter[6] illustrate how motion priors can be integrated, while Lumiere[10] exemplifies architectural advances for temporal consistency. A particularly active line of research explores how to embed physical constraints directly into the training or inference loop, balancing realism with generation flexibility. Physics Guided Motion Loss[0] sits squarely within the Physics-Based Loss Functions cluster, introducing differentiable penalties that enforce Newtonian principles during diffusion sampling. This approach contrasts with methods like 4real[5] and Physdiff[7], which also leverage physics-based losses but may differ in how they parameterize physical quantities or integrate them with pretrained models. Meanwhile, works such as Reindiffuse[3] and Enhancing Physical Plausibility[4] explore complementary strategies—Reindiffuse[3] emphasizes iterative refinement of motion trajectories, while Enhancing Physical Plausibility[4] may incorporate broader perceptual or simulation-based checks. The central tension across these branches is whether to hardcode physical rules, learn them from data, or blend both paradigms, with Physics Guided Motion Loss[0] representing a direct intervention strategy that complements the more data-driven motion prior approaches seen elsewhere in the taxonomy.

Claimed Contributions

Frequency-domain physics prior framework for video generation

The authors introduce a unified SIM(2) spectral framework that decomposes rigid motions (translation, rotation, scaling) into simple frequency-domain patterns. This framework provides global cues that are more tolerant to brightness variations and rendering errors compared to pixel-level approaches.

9 retrieved papers
Differentiable motion-aware regularizer with adaptive weighting

The authors develop a lightweight spectral loss that uses only 2.7% of frequency coefficients while preserving over 97% of spectral energy. The regularizer includes adaptive weighting based on maximum-entropy principles to handle videos with multiple motion patterns without requiring explicit motion classification.

1 retrieved paper
Drop-in regularizer improving motion quality across multiple diffusion backbones

The authors demonstrate that their method can be applied to existing video diffusion models (Open-Sora, MVDIT, Hunyuan) without architectural modifications, consistently improving motion accuracy and action recognition by approximately 11% on average while maintaining visual quality.

9 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Frequency-domain physics prior framework for video generation

The authors introduce a unified SIM(2) spectral framework that decomposes rigid motions (translation, rotation, scaling) into simple frequency-domain patterns. This framework provides global cues that are more tolerant to brightness variations and rendering errors compared to pixel-level approaches.

Contribution

Differentiable motion-aware regularizer with adaptive weighting

The authors develop a lightweight spectral loss that uses only 2.7% of frequency coefficients while preserving over 97% of spectral energy. The regularizer includes adaptive weighting based on maximum-entropy principles to handle videos with multiple motion patterns without requiring explicit motion classification.

Contribution

Drop-in regularizer improving motion quality across multiple diffusion backbones

The authors demonstrate that their method can be applied to existing video diffusion models (Open-Sora, MVDIT, Hunyuan) without architectural modifications, consistently improving motion accuracy and action recognition by approximately 11% on average while maintaining visual quality.

Table of Contents

Physics-Guided Motion Loss for Video Generation Model | Novelty Validation