Orionnnn/hub-stats huggingface dataset:
RobustSplat: Decoupling Densification and Dynamics for Transient-Free
  3DGS
Authors: name: Chuanyu Fu, name: Yuqi Zhang, name: Kunbin Yao, name: Guanying Chen, name: Yuan Xiong, name: Chuan Huang, name: Shuguang Cui, name: Xiaochun Cao
Published: 2025-06-03 11:13:48

3D Gaussian Splatting (3DGS) has gained significant attention for its
real-time, photo-realistic rendering in novel-view synthesis and 3D modeling.
However, existing methods struggle with accurately modeling scenes affected by
transient objects, leading to artifacts in the rendered images. We identify
that the Gaussian densification process, while enhancing scene detail capture,
unintentionally contributes to these artifacts by growing additional Gaussians
that model transient disturbances. To address this, we propose RobustSplat, a
robust solution based on two critical designs. First, we introduce a delayed
Gaussian growth strategy that prioritizes optimizing static scene structure
before allowing Gaussian splitting/cloning, mitigating overfitting to transient
objects in early optimization. Second, we design a scale-cascaded mask
bootstrapping approach that first leverages lower-resolution feature similarity
supervision for reliable initial transient mask estimation, taking advantage of
its stronger semantic consistency and robustness to noise, and then progresses
to high-resolution supervision to achieve more precise mask prediction.
Extensive experiments on multiple challenging datasets show that our method
outperforms existing methods, clearly demonstrating the robustness and
effectiveness of our method. Our project page is
https://fcyycf.github.io/RobustSplat/.
SeedVR2: One-Step Video Restoration via Diffusion Adversarial
  Post-Training
Authors: name: Jianyi Wang, name: Shanchuan Lin, name: Zhijie Lin, name: Yuxi Ren, name: Meng Wei, name: Zongsheng Yue, name: Shangchen Zhou, name: Hao Chen, name: Yang Zhao, name: Ceyuan Yang, name: Xuefeng Xiao, name: Chen Change Loy, name: Lu Jiang
Published: 2025-06-05 17:51:05

Recent advances in diffusion-based video restoration (VR) demonstrate
significant improvement in visual quality, yet yield a prohibitive
computational cost during inference. While several distillation-based
approaches have exhibited the potential of one-step image restoration,
extending existing approaches to VR remains challenging and underexplored,
particularly when dealing with high-resolution video in real-world settings. In
this work, we propose a one-step diffusion-based VR model, termed as SeedVR2,
which performs adversarial VR training against real data. To handle the
challenging high-resolution VR within a single step, we introduce several
enhancements to both model architecture and training procedures. Specifically,
an adaptive window attention mechanism is proposed, where the window size is
dynamically adjusted to fit the output resolutions, avoiding window
inconsistency observed under high-resolution VR using window attention with a
predefined window size. To stabilize and improve the adversarial post-training
towards VR, we further verify the effectiveness of a series of losses,
including a proposed feature matching loss without significantly sacrificing
training efficiency. Extensive experiments show that SeedVR2 can achieve
comparable or even better performance compared with existing VR approaches in a
single step.
PATS: Proficiency-Aware Temporal Sampling for Multi-View Sports Skill
  Assessment
Authors: name: Edoardo Bianchi, name: Antonio Liotta
Published: 2025-06-05 13:05:23

Automated sports skill assessment requires capturing fundamental movement
patterns that distinguish expert from novice performance, yet current video
sampling methods disrupt the temporal continuity essential for proficiency
evaluation. To this end, we introduce Proficiency-Aware Temporal Sampling
(PATS), a novel sampling strategy that preserves complete fundamental movements
within continuous temporal segments for multi-view skill assessment. PATS
adaptively segments videos to ensure each analyzed portion contains full
execution of critical performance components, repeating this process across
multiple segments to maximize information coverage while maintaining temporal
coherence. Evaluated on the EgoExo4D benchmark with SkillFormer, PATS surpasses
the state-of-the-art accuracy across all viewing configurations (+0.65% to
+3.05%) and delivers substantial gains in challenging domains (+26.22%
bouldering, +2.39% music, +1.13% basketball). Systematic analysis reveals that
PATS successfully adapts to diverse activity characteristics-from
high-frequency sampling for dynamic sports to fine-grained segmentation for
sequential skills-demonstrating its effectiveness as an adaptive approach to
temporal sampling that advances automated skill assessment for real-world
applications.
Video World Models with Long-term Spatial Memory
Authors: name: Tong Wu, name: Shuai Yang, name: Ryan Po, name: Yinghao Xu, name: Ziwei Liu, name: Dahua Lin, name: Gordon Wetzstein
Published: 2025-06-05 17:42:34

Emerging world models autoregressively generate video frames in response to
actions, such as camera movements and text prompts, among other control
signals. Due to limited temporal context window sizes, these models often
struggle to maintain scene consistency during revisits, leading to severe
forgetting of previously generated environments. Inspired by the mechanisms of
human memory, we introduce a novel framework to enhancing long-term consistency
of video world models through a geometry-grounded long-term spatial memory. Our
framework includes mechanisms to store and retrieve information from the
long-term spatial memory and we curate custom datasets to train and evaluate
world models with explicitly stored 3D memory mechanisms. Our evaluations show
improved quality, consistency, and context length compared to relevant
baselines, paving the way towards long-term consistent world generation.
The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly
  Licensed Text
Authors: name: Nikhil Kandpal, name: Brian Lester, name: Colin Raffel, name: Sebastian Majstorovic, name: Stella Biderman, name: Baber Abbasi, name: Luca Soldaini, name: Enrico Shippole, name: A. Feder Cooper, name: Aviya Skowron, name: John Kirchenbauer, name: Shayne Longpre, name: Lintang Sutawika, name: Alon Albalak, name: Zhenlin Xu, name: Guilherme Penedo, name: Loubna Ben Allal, name: Elie Bakouch, name: John David Pressman, name: Honglu Fan, name: Dashiell Stander, name: Guangyu Song, name: Aaron Gokaslan, name: Tom Goldstein, name: Brian R. Bartoldson, name: Bhavya Kailkhura, name: Tyler Murray
Published: 2025-06-05 16:21:30

Large language models (LLMs) are typically trained on enormous quantities of
unlicensed text, a practice that has led to scrutiny due to possible
intellectual property infringement and ethical concerns. Training LLMs on
openly licensed text presents a first step towards addressing these issues, but
prior data collection efforts have yielded datasets too small or low-quality to
produce performant LLMs. To address this gap, we collect, curate, and release
the Common Pile v0.1, an eight terabyte collection of openly licensed text
designed for LLM pretraining. The Common Pile comprises content from 30 sources
that span diverse domains including research papers, code, books,
encyclopedias, educational materials, audio transcripts, and more. Crucially,
we validate our efforts by training two 7 billion parameter LLMs on text from
the Common Pile: Comma v0.1-1T and Comma v0.1-2T, trained on 1 and 2 trillion
tokens respectively. Both models attain competitive performance to LLMs trained
on unlicensed text with similar computational budgets, such as Llama 1 and 2
7B. In addition to releasing the Common Pile v0.1 itself, we also release the
code used in its creation as well as the training mixture and checkpoints for
the Comma v0.1 models.
BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View
  Representations
Authors: name: Weiduo Yuan, name: Jerry Li, name: Justin Yue, name: Divyank Shah, name: Konstantinos Karydis, name: Hang Qiu
Published: 2025-06-03 08:07:18

Accurate LiDAR-camera calibration is fundamental to fusing multi-modal
perception in autonomous driving and robotic systems. Traditional calibration
methods require extensive data collection in controlled environments and cannot
compensate for the transformation changes during the vehicle/robot movement. In
this paper, we propose the first model that uses bird's-eye view (BEV) features
to perform LiDAR camera calibration from raw data, termed BEVCALIB. To achieve
this, we extract camera BEV features and LiDAR BEV features separately and fuse
them into a shared BEV feature space. To fully utilize the geometric
information from the BEV feature, we introduce a novel feature selector to
filter the most important features in the transformation decoder, which reduces
memory consumption and enables efficient training. Extensive evaluations on
KITTI, NuScenes, and our own dataset demonstrate that BEVCALIB establishes a
new state of the art. Under various noise conditions, BEVCALIB outperforms the
best baseline in the literature by an average of (47.08%, 82.32%) on KITTI
dataset, and (78.17%, 68.29%) on NuScenes dataset, in terms of (translation,
rotation), respectively. In the open-source domain, it improves the best
reproducible baseline by one order of magnitude. Our code and demo results are
available at https://cisl.ucr.edu/BEVCalib.
VideoMathQA: Benchmarking Mathematical Reasoning via Multimodal
  Understanding in Videos
Authors: name: Hanoona Rasheed, name: Abdelrahman Shaker, name: Anqi Tang, name: Muhammad Maaz, name: Ming-Hsuan Yang, name: Salman Khan, name: Fahad Khan
Published: 2025-06-05 17:59:58

Mathematical reasoning in real-world video settings presents a fundamentally
different challenge than in static images or text. It requires interpreting
fine-grained visual information, accurately reading handwritten or digital
text, and integrating spoken cues, often dispersed non-linearly over time. In
such multimodal contexts, success hinges not just on perception, but on
selectively identifying and integrating the right contextual details from a
rich and noisy stream of content. To this end, we introduce VideoMathQA, a
benchmark designed to evaluate whether models can perform such temporally
extended cross-modal reasoning on videos. The benchmark spans 10 diverse
mathematical domains, covering videos ranging from 10 seconds to over 1 hour.
It requires models to interpret structured visual content, understand
instructional narratives, and jointly ground concepts across visual, audio, and
textual modalities. We employ graduate-level experts to ensure high quality,
totaling over 920 man-hours of annotation. To reflect real-world scenarios,
questions are designed around three core reasoning challenges: direct problem
solving, where answers are grounded in the presented question; conceptual
transfer, which requires applying learned methods to new problems; and deep
instructional comprehension, involving multi-step reasoning over extended
explanations and partially worked-out solutions. Each question includes
multi-step reasoning annotations, enabling fine-grained diagnosis of model
capabilities. Through this benchmark, we highlight the limitations of existing
approaches and establish a systematic evaluation framework for models that must
reason, rather than merely perceive, across temporally extended and
modality-rich mathematical problem settings. Our benchmark and evaluation code
are available at: https://mbzuai-oryx.github.io/VideoMathQA
Autoregressive Images Watermarking through Lexical Biasing: An Approach
  Resistant to Regeneration Attack
Authors: name: Siqi Hui, name: Yiren Song, name: Sanping Zhou, name: Ye Deng, name: Wenli Huang, name: Jinjun Wang
Published: 2025-06-01 13:44:20

Autoregressive (AR) image generation models have gained increasing attention
for their breakthroughs in synthesis quality, highlighting the need for robust
watermarking to prevent misuse. However, existing in-generation watermarking
techniques are primarily designed for diffusion models, where watermarks are
embedded within diffusion latent states. This design poses significant
challenges for direct adaptation to AR models, which generate images
sequentially through token prediction. Moreover, diffusion-based regeneration
attacks can effectively erase such watermarks by perturbing diffusion latent
states. To address these challenges, we propose Lexical Bias Watermarking
(LBW), a novel framework designed for AR models that resists regeneration
attacks. LBW embeds watermarks directly into token maps by biasing token
selection toward a predefined green list during generation. This approach
ensures seamless integration with existing AR models and extends naturally to
post-hoc watermarking. To increase the security against white-box attacks,
instead of using a single green list, the green list for each image is randomly
sampled from a pool of green lists. Watermark detection is performed via
quantization and statistical analysis of the token distribution. Extensive
experiments demonstrate that LBW achieves superior watermark robustness,
particularly in resisting regeneration attacks.
Micro-Act: Mitigate Knowledge Conflict in Question Answering via
  Actionable Self-Reasoning
Authors: name: Nan Huo, name: Jinyang Li, name: Bowen Qin, name: Ge Qu, name: Xiaolong Li, name: Xiaodong Li, name: Chenhao Ma, name: Reynold Cheng
Published: 2025-06-05 17:33:02

Retrieval-Augmented Generation (RAG) systems commonly suffer from Knowledge
Conflicts, where retrieved external knowledge contradicts the inherent,
parametric knowledge of large language models (LLMs). It adversely affects
performance on downstream tasks such as question answering (QA). Existing
approaches often attempt to mitigate conflicts by directly comparing two
knowledge sources in a side-by-side manner, but this can overwhelm LLMs with
extraneous or lengthy contexts, ultimately hindering their ability to identify
and mitigate inconsistencies. To address this issue, we propose Micro-Act a
framework with a hierarchical action space that automatically perceives context
complexity and adaptively decomposes each knowledge source into a sequence of
fine-grained comparisons. These comparisons are represented as actionable
steps, enabling reasoning beyond the superficial context. Through extensive
experiments on five benchmark datasets, Micro-Act consistently achieves
significant increase in QA accuracy over state-of-the-art baselines across all
5 datasets and 3 conflict types, especially in temporal and semantic types
where all baselines fail significantly. More importantly, Micro-Act exhibits
robust performance on non-conflict questions simultaneously, highlighting its
practical value in real-world RAG applications.
FlexPainter: Flexible and Multi-View Consistent Texture Generation
Authors: name: Dongyu Yan, name: Leyi Wu, name: Jiantao Lin, name: Luozhou Wang, name: Tianshuo Xu, name: Zhifei Chen, name: Zhen Yang, name: Lie Xu, name: Shunsi Zhang, name: Yingcong Chen
Published: 2025-06-03 08:36:03

Texture map production is an important part of 3D modeling and determines the
rendering quality. Recently, diffusion-based methods have opened a new way for
texture generation. However, restricted control flexibility and limited prompt
modalities may prevent creators from producing desired results. Furthermore,
inconsistencies between generated multi-view images often lead to poor texture
generation quality. To address these issues, we introduce FlexPainter,
a novel texture generation pipeline that enables flexible multi-modal
conditional guidance and achieves highly consistent texture generation. A
shared conditional embedding space is constructed to perform flexible
aggregation between different input modalities. Utilizing such embedding space,
we present an image-based CFG method to decompose structural and style
information, achieving reference image-based stylization. Leveraging the 3D
knowledge within the image diffusion prior, we first generate multi-view images
simultaneously using a grid representation to enhance global understanding.
Meanwhile, we propose a view synchronization and adaptive weighting module
during diffusion sampling to further ensure local consistency. Finally, a
3D-aware texture completion model combined with a texture enhancement model is
used to generate seamless, high-resolution texture maps. Comprehensive
experiments demonstrate that our framework significantly outperforms
state-of-the-art methods in both flexibility and generation quality.
SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video
  Diffusion Transformers
Authors: name: Zhengcong Fei, name: Hao Jiang, name: Di Qiu, name: Baoxuan Gu, name: Youqiang Zhang, name: Jiahua Wang, name: Jialin Bai, name: Debang Li, name: Mingyuan Fan, name: Guibin Chen, name: Yahui Zhou
Published: 2025-06-01 04:27:13

The generation and editing of audio-conditioned talking portraits guided by
multimodal inputs, including text, images, and videos, remains under explored.
In this paper, we present SkyReels-Audio, a unified framework for synthesizing
high-fidelity and temporally coherent talking portrait videos. Built upon
pretrained video diffusion transformers, our framework supports infinite-length
generation and editing, while enabling diverse and controllable conditioning
through multimodal inputs. We employ a hybrid curriculum learning strategy to
progressively align audio with facial motion, enabling fine-grained multimodal
control over long video sequences. To enhance local facial coherence, we
introduce a facial mask loss and an audio-guided classifier-free guidance
mechanism. A sliding-window denoising approach further fuses latent
representations across temporal segments, ensuring visual fidelity and temporal
consistency across extended durations and diverse identities. More importantly,
we construct a dedicated data pipeline for curating high-quality triplets
consisting of synchronized audio, video, and textual descriptions.
Comprehensive benchmark evaluations show that SkyReels-Audio achieves superior
performance in lip-sync accuracy, identity consistency, and realistic facial
dynamics, particularly under complex and challenging conditions.
ComfyUI-Copilot: An Intelligent Assistant for Automated Workflow
  Development
Authors: name: Zhenran Xu, name: Xue Yang, name: Yiyu Wang, name: Qingli Hu, name: Zijiao Wu, name: Longyue Wang, name: Weihua Luo, name: Kaifu Zhang, name: Baotian Hu, name: Min Zhang
Published: 2025-06-05 13:20:50

We introduce ComfyUI-Copilot, a large language model-powered plugin designed
to enhance the usability and efficiency of ComfyUI, an open-source platform for
AI-driven art creation. Despite its flexibility and user-friendly interface,
ComfyUI can present challenges to newcomers, including limited documentation,
model misconfigurations, and the complexity of workflow design. ComfyUI-Copilot
addresses these challenges by offering intelligent node and model
recommendations, along with automated one-click workflow construction. At its
core, the system employs a hierarchical multi-agent framework comprising a
central assistant agent for task delegation and specialized worker agents for
different usages, supported by our curated ComfyUI knowledge bases to
streamline debugging and deployment. We validate the effectiveness of
ComfyUI-Copilot through both offline quantitative evaluations and online user
feedback, showing that it accurately recommends nodes and accelerates workflow
development. Additionally, use cases illustrate that ComfyUI-Copilot lowers
entry barriers for beginners and enhances workflow efficiency for experienced
users. The ComfyUI-Copilot installation package and a demo video are available
at https://github.com/AIDC-AI/ComfyUI-Copilot.
Unfolding Spatial Cognition: Evaluating Multimodal Models on Visual
  Simulations
Authors: name: Linjie Li, name: Mahtab Bigverdi, name: Jiawei Gu, name: Zixian Ma, name: Yinuo Yang, name: Ziang Li, name: Yejin Choi, name: Ranjay Krishna
Published: 2025-06-05 05:09:46

Spatial cognition is essential for human intelligence, enabling
problem-solving through visual simulations rather than solely relying on verbal
reasoning. However, existing AI benchmarks primarily assess verbal reasoning,
neglecting the complexities of non-verbal, multi-step visual simulation. We
introduce STARE(Spatial Transformations and Reasoning Evaluation), a benchmark
designed to rigorously evaluate multimodal large language models on tasks
better solved through multi-step visual simulation. STARE features 4K tasks
spanning foundational geometric transformations (2D and 3D), integrated spatial
reasoning (cube net folding and tangram puzzles), and real-world spatial
reasoning (perspective and temporal reasoning), reflecting practical cognitive
challenges like object assembly, mechanical diagram interpretation, and
everyday spatial navigation. Our evaluations show that models excel at
reasoning over simpler 2D transformations, but perform close to random chance
on more complex tasks like 3D cube net folding and tangram puzzles that require
multi-step visual simulations. Humans achieve near-perfect accuracy but take
considerable time (up to 28.9s) on complex tasks, significantly speeding up
(down by 7.5 seconds on average) with intermediate visual simulations. In
contrast, models exhibit inconsistent performance gains from visual
simulations, improving on most tasks but declining in specific cases like
tangram puzzles (GPT-4o, o1) and cube net folding (Claude-3.5, Gemini-2.0
Flash), indicating that models may not know how to effectively leverage
intermediate visual information.
MINT-CoT: Enabling Interleaved Visual Tokens in Mathematical
  Chain-of-Thought Reasoning
Authors: name: Xinyan Chen, name: Renrui Zhang, name: Dongzhi Jiang, name: Aojun Zhou, name: Shilin Yan, name: Weifeng Lin, name: Hongsheng Li
Published: 2025-06-05 17:59:02

Chain-of-Thought (CoT) has widely enhanced mathematical reasoning in Large
Language Models (LLMs), but it still remains challenging for extending it to
multimodal domains. Existing works either adopt a similar textual reasoning for
image input, or seek to interleave visual signals into mathematical CoT.
However, they face three key limitations for math problem-solving: reliance on
coarse-grained box-shaped image regions, limited perception of vision encoders
on math content, and dependence on external capabilities for visual
modification. In this paper, we propose MINT-CoT, introducing Mathematical
INterleaved Tokens for Chain-of-Thought visual reasoning. MINT-CoT adaptively
interleaves relevant visual tokens into textual reasoning steps via an
Interleave Token, which dynamically selects visual regions of any shapes within
math figures. To empower this capability, we construct the MINT-CoT dataset,
containing 54K mathematical problems aligning each reasoning step with visual
regions at the token level, accompanied by a rigorous data generation pipeline.
We further present a three-stage MINT-CoT training strategy, progressively
combining text-only CoT SFT, interleaved CoT SFT, and interleaved CoT RL, which
derives our MINT-CoT-7B model. Extensive experiments demonstrate the
effectiveness of our method for effective visual interleaved reasoning in
mathematical domains, where MINT-CoT-7B outperforms the baseline model by
+34.08% on MathVista, +28.78% on GeoQA, and +23.2% on MMStar, respectively. Our
code and data are available at https://github.com/xinyan-cxy/MINT-CoT