Hao Tang

Geometry-consistency awareness in video spatial reasoning can lead to a 12.6-point performance boost over current state-of-the-art models.

Geometry-aware reconstruction reduces hallucinations in object poses, leading to more reliable robot planning.

Factorized Neural Operators achieve superior physical modeling by separating transient and persistent responses, leading to enhanced accuracy and interpretability.

DragMesh-2 achieves unprecedented robustness in dexterous manipulation of articulated objects, outperforming traditional methods even under varying contact loads.

A dual-stream tokenizer reveals that traditional methods misrepresent high-frequency motion dynamics, leading to over 50% improvement in diversity alignment with real data.

Forget fragmented architectures: UniMesh lets you edit 3D meshes iteratively with semantic prompts, bridging the gap between generation and understanding.

Encoding scene graphs in hyperbolic space unlocks significantly better hierarchical structure quality compared to Euclidean embeddings, leading to state-of-the-art graph-level scene understanding.

MLLMs struggle with spatial reasoning because they flatten geometric information too early; GUIDE fixes this by progressively injecting multi-granularity geometric priors into early layers, leading to substantial performance gains.

By explicitly enforcing action-conditioned consistency during training and distillation, MWM enables more reliable planning in imagined future spaces for embodied navigation.

By swapping out ConvGRUs for a novel ConvSS2D operator, StereoAdapter-2 achieves state-of-the-art zero-shot underwater stereo depth estimation with a single update step.

Achieve zero-shot robotic manipulation by guiding a hierarchical vision-language-action model with knowledge-guided trajectory planning, outperforming existing methods and eliminating the need for real-world data collection.