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Stop avatars from looking like they're having a seizure: this method uses autoregressive prediction of appearance latents to create temporally stable and high-fidelity 3D Gaussian avatars.
By disentangling camera-space estimation from world-space refinement via dual diffusion models, DuoMo achieves state-of-the-art human motion reconstruction from noisy video, bypassing the limitations of parametric models.
Finally, VR agents can maintain natural gaze and respond to user movement in real-time, thanks to a novel architecture that achieves state-of-the-art motion quality at over 300 FPS.
