CausalAlign: Aligning Causal Representations of Video Generation Models for Stable Camera Ego-Motion

CausalAlign proposes a causal alignment framework that uses linear probes and causal interventions to locate the representation layers for camera ego-motion in video generation models, then aligns the representations at those layers to a 3D foundation model via delta-token space. After fine-tuning Wan2.2 on DL3DV and RealEstate10K, geometric consistency improves by more than 20% over the base model, and on RoboCasa365 mobile manipulation tasks it raises the success rate of the world action model (WAM) by 10 percentage points.