Fluid-Gen-Zero: Grounding Pretrained Video Generators in Physics without Training

arXiv:2610.10984 · cs.CV, cs.GR · Submitted 2026-10-07 · Read on arXiv

cs.CV, cs.GR

Submitted: 2026-10-07

Updated: 2026-10-07

Code: https://github.com/alimamacreative/FLUX-Controlnet-Inpainting

Terminology

• Benchmark — 20× in this paper · explained in 2 episodes
• Alignment — 19× in this paper · explained in 1 episode
• Consistency — 17× in this paper · explained in 3 episodes
• Optical Flow — 14× in this paper · explained in 2 episodes
• Grounding — 8× in this paper · explained in 3 episodes
• Temporal consistency — 7× in this paper · explained in 2 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Training-Free Framework — 4× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Fidelity — 2× in this paper · explained in 2 episodes
• Prompt Adherence — 2× in this paper · explained in 2 episodes
• Transactions on Graphics — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Domain Walls — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Cosmos World Foundation Model Platform for Physical AI
• VideoPhy: Evaluating Physical Commonsense for Video Generation
• PhyGDPO: Physics-Aware Groupwise Direct Preference Optimization for Physically Consistent Text-to-Video Generation
• Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance
• Physical Simulator In-the-Loop Video Generation
• PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by Watching Stuff Drop
• MotionClone: Training-Free Motion Cloning for Controllable Video Generation
• Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
• RealWonder: Real-Time Physical Action-Conditioned Video Generation
• SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
• Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation
• SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation
• SAM 2: Segment Anything in Images and Videos
• Time-to-Move: Training-Free Motion Controlled Video Generation via Dual-Clock Denoising
• ATI: Any Trajectory Instruction for Controllable Video Generation
• WISA: World Simulator Assistant for Physics-Aware Text-to-Video Generation
• PhysAnimator: Physics-Guided Generative Cartoon Animation
• PerpetualWonder: Long-Horizon Action-Conditioned 4D Scene Generation

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