JD‑Tech leveraged large‑model innovations and a novel AnchorDP3 3D diffusion policy to win both stages of the CVPR 2025 dual‑arm manipulation competition, showcasing breakthroughs in synthetic data generation, multimodal perception, and precise trajectory control for embodied AI robots.
The CVPR 2025 awards spotlight groundbreaking work—from the VGGT transformer that predicts full 3D scenes in a single feed‑forward pass to neural inverse rendering that reconstructs geometry from time‑resolved light—offering a comprehensive view of emerging trends, novel architectures, and performance breakthroughs across computer‑vision research.
The CVPR 2025 conference announced record‑breaking submission numbers, awarded a Best Paper to VGGT, honored young researchers and student papers, listed multiple honorary nominations, and highlighted the strong presence of Chinese institutions across the award candidates.
Meituan presented 10 papers at CVPR 2025 and ICLR 2025, featuring breakthroughs in LLMs, multimodal generation, and computer vision, such as ternary preference alignment, temporal-decay DPO, joint-embedding architectures, 4-bit quantization, and spatial-temporal models, emphasizing model efficiency, cross-modal alignment, and high-resolution synthesis, and will host an ICLR 2025 booth in Singapore for collaboration and knowledge exchange.
The paper introduces a globally first trainable HVI color space and a lightweight CIDNet network that jointly model intensity and chrominance, eliminating color bias and brightness artifacts in low‑light image enhancement and achieving state‑of‑the‑art results on ten benchmark datasets.
