Shuaicheng Liu

FutureRTC transforms real-time robot execution by seamlessly predicting execution-time observations, leading to smoother and faster task performance even under inference delays.

Transforming image generation into a dynamic feedback system yields up to 25.36% better facial similarity and significant spatial error reductions without any training.

CamFlow+ achieves superior 2D camera motion estimation and stabilization by seamlessly integrating multiple motion bases, outperforming traditional methods in complex environments.

Achieve 10x faster visuomotor control by shifting iterative refinement from slow inference to efficient training, preserving multimodal action fidelity in single-step robotic manipulation.

SeedPolicy overcomes the long-horizon limitations of Diffusion Policies in robot manipulation by compressing temporal information with a novel gated attention mechanism, achieving state-of-the-art imitation learning performance with significantly fewer parameters than vision-language-action models.

Ditch optical flow for image alignment: DMAligner leverages diffusion models to synthesize novel views, sidestepping traditional warping's occlusion and illumination woes.

Unlock zero-shot 3D shape completion across diverse partial observations by exploiting geometric priors from 3D foundation models, without any training.