Long Chen

SPG-Layout achieves a breakthrough in 3D scene synthesis by generating physically plausible layouts in non-Manhattan environments, outperforming existing methods.

FlowCIR slashes training resource requirements by 90% while boosting robustness against negation in zero-shot image retrieval tasks.

AVTok achieves superior audio-video synchronization and reconstruction, setting a new standard for unified multimodal generation.

LISA accelerates training and enhances output quality in visual-condition generation by aligning side network features with likelihood scores, all without extra inference costs.

Advanced RAG methods like GraphRAG and Agentic RAG can reduce token usage by up to 53%, but they don't always enhance generation quality as expected.

SPAR bridges the critical gap between semantic perception and pixel-level generation, achieving unprecedented quality in visual outputs without external supervision.

CP4D achieves photorealistic 4D scene generation by seamlessly integrating static environments with dynamic objects, outperforming existing methods in visual fidelity and physical consistency.

OneVLA unifies navigation and manipulation tasks into a single framework, enabling robots to seamlessly interpret commands and interact with their environments like never before.

Fine-grained 3D object grounding gets a boost: SSR3D-LLM uses latent spatial reasoning steps to iteratively refine candidate rankings, outperforming single-pointer methods and setting a new standard for unified 3D-LLMs.

Decoupling radial and angular dynamics in vision-language model adaptation unlocks significant gains in few-shot performance, outperforming existing flow matching methods.