Chang D. Yoo

TanGO achieves state-of-the-art 3D editing by allowing fine-grained control over individual tokens, drastically reducing semantic artifacts.

Novel-view fidelity in drawing-based 3D animation can be drastically improved with a lightweight module that outperforms traditional fine-tuning methods.

GADA corrects spatial misalignments in Gaussian Splatting, preserving high-frequency details while achieving over twice the processing speed of existing techniques.

RTFree-F5 achieves a remarkable 10.4% WER on dysarthric speech without needing any reference transcripts, surpassing even ground-truth baselines.

VOTP slashes the labeling burden in preference-based reinforcement learning, achieving superior performance with minimal human input.

Single-step action generation can outperform multi-step diffusion methods in offline reinforcement learning, achieving higher performance with lower computational costs.

Diffusion Transformers waste up to 66% of their conditional embedding space without sacrificing generation quality, hinting at opportunities for more efficient conditioning.

Unlock the potential of your offline RL data: a new framework achieves state-of-the-art performance on D4RL benchmarks by quantifying and leveraging data uncertainty with a computationally efficient Rank-One MIMO architecture.