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PhysisForcing is a training framework that enhances embodied video generation for robotic manipulation by enforcing physical consistency through pixel-level trajectory alignment and semantic-level relational alignment losses in a DiT-based architecture, achieving notable improvements on benchmarks.
PhaseLock is a training-free framework that preserves motion priors from early-step inference to improve physical consistency in image-to-video diffusion models, achieving 6.2 point improvement with minimal overhead.
CRONOS is a benchmark that evaluates counterfactual physical consistency in video prediction models by intervening on viewpoint, scene, object category, and appearance while keeping physical event types fixed. It reveals substantial failures in current video generators.
CoInteract introduces an end-to-end Diffusion Transformer framework that jointly models RGB appearance and HOI geometry to generate physically-plausible human-object interaction videos with stable hands/faces and zero inference overhead.