跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+1已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-08 20:45 · 全站最近入库：2026-11-08 06:02各信源进度 →

统计口径与数据新鲜度

统计 2026-11-07 21:41 — 2026-11-08 21:41（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-08 20:45最近发现新增版本：2026-11-08 06:02库内论文最新更新时间：2026-11-06 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

17,100 已收录论文14,661 AI 相关4,057 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 3 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-08 20:44（北京时间）
首次观测 · 记录 #1CVF · CVPR 2026:cvf:a6ce8828910f4bfba7bb4378573feb49CVF · CVPR 2026

S2D: Sparse to Dense Lifting for 3D Reconstruction with Minimal Inputs

Ji, Yuzhou, Tian, Qijian, Zhu, He, Jiang, Xiaoqi, Cao, Guangzhi, Ma, Lizhuang, Xie, Yuan, Tan, Xin

原文摘要

Explicit 3D representations have already become an essential medium for 3D simulation and understanding. However, the most commonly used point cloud and 3D Gaussian Splatting (3DGS) each suffer from non-photorealistic rendering and significant degradation under sparse inputs. In this paper, we introduce Sparse to Dense lifting (S2D), a novel pipeline that bridges the two representations and achieves high-quality 3DGS reconstruction with minimal inputs. Specifically, the S2D lifting is two-fold. We first present an efficient one-step diffusion model that lifts sparse point cloud for high-fidelity image artifact fixing. Meanwhile, to reconstruct 3D consistent scenes, we also design a corresponding reconstruction strategy with random sample drop and weighted gradient for robust model fitting from sparse input views to dense novel views. Extensive experiments show that S2D achieves the best consistency in generating novel view guidance and first-tier sparse view reconstruction quality under different input sparsity. By reconstructing stable scenes with the least possible captures among existing methods, S2D enables minimal input requirements for 3DGS applications.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:f1f624e1ca4a7b9bbd825c2e605d1e81CVF · CVPR 2026

Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration

Wang, Sen, Liu, Bangwei, Gao, Zhenkun, Ma, Lizhuang, Wang, Xuhong, Xie, Yuan, Tan, Xin

原文摘要

An ideal embodied agent should possess lifelong learning capabilities to handle long-horizon and complex tasks, enabling continuous operation in general environments. This not only requires the agent to accurately accomplish given tasks but also to leverage long-term episodic memory to optimize decision-making. However, existing mainstream one-shot embodied tasks primarily focus on task completion results, neglecting the crucial process of exploration and memory utilization. To address this, we propose Long-term Memory Embodied Exploration (LMEE), which aims to unify the agent's exploratory cognition and decision-making behaviors to promote lifelong learning. We further construct a corresponding dataset and benchmark, LMEE-Bench, incorporating multi-goal navigation and memory-based question answering to comprehensively evaluate both the process and outcome of embodied exploration. To enhance the agent's memory recall and proactive exploration capabilities, we propose MemoryExplorer, a novel method that fine-tunes a multimodal large language model through reinforcement learning to encourage active memory querying. By incorporating a multi-task reward function that includes action prediction, frontier selection, and question answering, our model achieves proactive exploration. Extensive experiments against state-of-the-art embodied exploration models demonstrate that our approach achieves significant advantages in long-horizon embodied tasks. Our dataset and code will be released at https://wangsen99.github.io/papers/lmee/

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:f30925695ccee05b9a3ffc43de4b508aCVF · CVPR 2026

Open the Motion Door: Atomic Motion Decomposition and Recomposition for Open-Vocabulary Motion Generation

Fan, Ke, Zhang, Jiangning, Yi, Ran, Gong, Jingyu, Wang, Yabiao, Wang, Yating, Tan, Xin, Wang, Chengjie 等 9 位作者

原文摘要

Text-to-motion generation is a fundamental task in computer vision, aiming to synthesize 3D human motion sequences from natural language descriptions. However, due to the limited scale and diversity of existing datasets, models trained to directly map raw text to motion often struggle to generalize to out-of-domain textual inputs. We observe that although high-level motion semantics vary widely, many motions share a common set of underlying atomic motions--that is, simple, reusable body-part movements. Building on this insight, we introduce an **Atomic Motion Decomposition and Recomposition** framework for open-vocabulary text-to-motion generation. Our approach consists of two key components: a **Textual Decomposition** module that parses out-of-domain descriptions into atomic motion units, and an **Atomic Recomposition** module that integrates these units to produce the final motion sequence. Our model achieves a competitive performance on the in-domain HumanML3D dataset, and extensive experiments on two out-of-domain datasets (IDEA400 and Mixamo) demonstrate that our method substantially outperforms state-of-the-art approaches in open-vocabulary motion generation.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗