Wei Deng

RS-Gen achieves state-of-the-art performance in image generation by autonomously identifying and resolving logical issues and knowledge gaps in real-time.

STAR Allocation enables targeted policy updates in text-to-image generation, leading to unprecedented improvements in semantic alignment and text rendering.

Autonomous code generation combined with rigorous semantic review can drastically enhance scenario mining accuracy in complex driving environments.

By rethinking text-to-3D generation as a planning problem, this approach significantly reduces error propagation and enhances scene realism.

Transforming VLMs into active agents with cognitive maps leads to a staggering 53.2% boost in spatial reasoning accuracy.

A unified benchmark reveals the trade-offs between pixel-wise accuracy and perceptual realism in state-of-the-art image super-resolution techniques.