arXiv AI By Lin Jiang, Rongchao Xu, Guang Wang

Cross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation

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arXiv Machine Learning
Oct 22

DriftNeRF: Cross-Ray Density Drifting for Sparse-View Neural Radiance Fields

DriftNeRF introduces a regularizer that addresses cross‑ray density disagreement in Neural Radiance Fields, a defect that causes poor performance when only a few views are available. By aggregating densities from nearby points on other rays into a stop‑gradient consensus target, DriftNeRF penalizes unsupported disagreement while preserving agreed boundaries. The method improves 3‑view PSNR by 0.42–0.65 dB on NeRF‑Synthetic, DTU, and LLFF datasets and remains competitive with sparse‑view Gaussian pipelines without requiring additional geometry or preprocessing.

By Juntang Wang, Yihan Wang, Sheng'en Li, Hao Wu, Dongmian Zou, Shixin Xu
arXiv Machine Learning
Aug 4

DAVET: Denoising-Aware Visual Evidence Trajectory Allocation for Diffusion Vision-Language Models

arXiv:2608. 01821v1 Announce Type: cross Abstract: Diffusion vision-language models (dVLMs) iteratively denoise masked responses while conditioning each denoising step on visual evidence, making visual conditioning a substantial recurring inference cost.

By Yongkang Zhou, Xiang Xia, Cheng Yan, Fan Xu, Wuyang Zhang