The ultimate goal of video generation is to satisfy a fundamental trilemma: achieving high visual quality, maintaining rigorous physical consistency, and enabling precise controllability. While recent models can maintain this balance in simple, isolated scenarios, we observe that this equilibrium is fragile and often breaks down as scene complexity increases (e.g., involving collisions or dense traffic). To address this, we introduce Motion Forcing, a framework designed to stabilize this trilemma even in complex generative tasks. Our key insight is to explicitly decouple physical reasoning from visual synthesis via a hierarchical ``Point-Shape-Appearance'' paradigm. This approach decomposes generation into verifiable stages: modeling complex dynamics as sparse geometric anchors (Point), expanding them into dynamic depth maps that explicitly resolve 3D geometry (Shape), and finally rendering high-fidelity textures (Appearance). Furthermore, to foster robust physical understanding, we employ a Masked Point Recovery strategy. By randomly masking input anchors during training and enforcing the reconstruction of complete dynamic depth, the model is compelled to move beyond passive pattern matching and learn latent physical laws (e.g., inertia) to infer missing trajectories. Extensive experiments on autonomous driving benchmarks show that Motion Forcing significantly outperforms state-of-the-art baselines, maintaining trilemma stability across complex scenes. Evaluations on physics and robotics further confirm our framework's generality.
Figures & tables
Figure 1: Motion Forcing takes user input motion and the first frame to generate motion-coherent future frames. (a-d) show reactive ego-trajectories responding to dangerous scenarios initiated by other vehicles. (e) shows a physics scene where different actions lead to different collision results. (f) shows embodied AI, where directional inputs control a robotic hand to move an object in corresponding directions.
Figure 2: Overview of the Motion Forcing framework. (a) Preparation of Motion Representations: Input control signals are processed and subjected to spatial and temporal masking before being fed into the model. (b) Training: The model is trained by randomly sampling between two independent stages: "Point → Shape" (depth generation) and "Shape → Appearance" (RGB rendering). (c) Inference: The generation follows a complete two-stage hierarchical process, sequentially mapping sparse points to structural depth (Shape), and finally to the target RGB video (Appearance).
Figure 3: Comparison with state-of-the-art models in synthesizing cut-in and evasive driving scenarios. While the leading text-conditioned models (Seed Dance2.0, Wan2.6, Vista) rely on text prompts, trajectory-controllable baselines like MOFA-Video require object trajectories coupled with scene trajectories to simulate camera movement. Our method Motion Forcing , however, effectively captures these complex dynamics given only the initial ego and object motion trajectories.
Figure 4: Validation of physical capabilities. ✓ indicate physically coherent. MOFA-Video is fine-tuned on our exact dataset for fairness. As the control commands become more complex, only our method maintains stable and physically coherent generation.
Figure 5: Controllable robotic hand manipulation. By providing different motion control inputs, our model can control the movement of the robotic arm and the grasped object in specified directions.
Figure 6: Qualitative comparison of ego motion control. Compared to the baseline AdaLN approach, our Depth Warping-based method demonstrates significantly superior accuracy and flexibility in controlling both the direction and speed.
Table 7
Appendix figures & tables3 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 7: Generation pipeline and point control robustness — Scene 1. Top row: shared camera-warped depth condition. Rows 2–10: three point control variants (A/B/C), each showing point input, generated depth, and generated RGB (5 uniformly sampled frames per row).
Figure 8: Generation pipeline and point control robustness — Scene 2. Same layout as Figure 7 . The model faithfully propagates structurally distinct trajectories through the depth stage before RGB rendering.
Figure 9: Generation pipeline and point control robustness — Scene 3. Same layout as Figure 7 . Across all three scenes the depth-first hierarchy cleanly separates physical reasoning from visual rendering, enabling robust control with diverse sparse inputs.