Despite recent advancements in Text-to-Video (T2V) synthesis, generating high-fidelity and dynamic motion remains a significant challenge. Existing methods primarily rely on Classifier-Free Guidance (CFG), but standard CFG treats all semantic dimensions uniformly and provides no mechanism to selectively resolve ambiguity along the motion axis. To address this, we propose MotionCFG, a training-free approach that introduces selective guidance along the motion subspace of the condition embedding. Specifically, we construct a motion-perturbed negative condition by injecting Gaussian noise into motion-related embeddings, and steer generation away from it to selectively amplify motion intent. We show theoretically that this procedure implicitly approximates Semantic Laplacian Sharpening, a second-order correction that suppresses motion-ambiguous regions of the score landscape and amplifies well-resolved dynamic peaks. Combined with a piecewise guidance schedule that confines intervention to the early denoising steps, MotionCFG consistently improves motion dynamics across state-of-the-art T2V frameworks with negligible overhead. We further demonstrate that this Laplacian sharpening principle generalizes beyond motion, effectively steering complex, non-linear concepts such as precise object numerosity that are typically difficult to modulate via standard text-based guidance. Our project page is available at https://motion-cfg.vercel.app/.
Byungjun Kim, Taeksoo Kim, Hyunsoo Cha, Hanbyul Joo
原文摘要
Action-conditioned video world models predict future observations from an initial observation and an action signal. We propose robot-factored world models (RoFacto), which move two robot-specific factors outside the world model to separate action realization from scene-response prediction. First, action realization: each command sequence is rolled through the robot's own controller and kinematics without scene contact into a deployment-available nominal trajectory that does not require future realized states. Second, robot rendering: this nominal trajectory is rendered through the robot URDF, factoring the robot's geometry, kinematics, and appearance out of the model and into explicit rendered robot geometry. Paired end-effector and scene depth provide geometric cues for contact and occlusion, while camera-aware static RGB context and rendered robot geometry form a shared visual interface across viewpoints and embodiments through which the model learns scene responses. Our experiments show that the rendered interface outperforms vector-conditioned baselines, supports transfer to unseen robot embodiments, and generates robot manipulation videos from retargeted human demonstrations.