Li Yu

Mengzhang Cai, Xin Gao, Yu Li et al.

The rapid evolution of Large Language Models (LLMs) is predicated on the quality and diversity of post-training datasets. However, a critical dichotomy persists: while models are rigorously benchmarked, the data fueling them remains a black box--characterized by opaque composition, uncertain provenance, and a lack of systematic evaluation. This opacity hinders reproducibility and obscures the causal link between data characteristics and model behaviors. To bridge this gap, we introduce OpenDataArena (ODA), a holistic and open platform designed to benchmark the intrinsic value of post-training data. ODA establishes a comprehensive ecosystem comprising four key pillars: (i) a unified training-evaluation pipeline that ensures fair, open comparisons across diverse models (e.g., Llama, Qwen) and domains; (ii) a multi-dimensional scoring framework that profiles data quality along tens of distinct axes; (iii) an interactive data lineage explorer to visualize dataset genealogy and dissect component sources; and (iv) a fully open-source toolkit for training, evaluation, and scoring to foster data research. Extensive experiments on ODA--covering over 120 training datasets across multiple domains on 22 benchmarks, validated by more than 600 training runs and 40 million processed data points--reveal non-trivial insights. Our analysis uncovers the inherent trade-offs between data complexity and task performance, identifies redundancy in popular benchmarks through lineage tracing, and maps the genealogical relationships across datasets. We release all results, tools, and configurations to democratize access to high-quality data evaluation. Rather than merely expanding a leaderboard, ODA envisions a shift from trial-and-error data curation to a principled science of Data-Centric AI, paving the way for rigorous studies on data mixing laws and the strategic composition of foundation models.

Yunfei Liu, Lei Zhu, Lijian Lin et al.

3D facial reconstruction from a single in-the-wild image is a crucial task in human-centered computer vision tasks. While existing methods can recover accurate facial shapes, there remains significant space for improvement in fine-grained expression capture. Current approaches struggle with irregular mouth shapes, exaggerated expressions, and asymmetrical facial movements. We present TEASER (Token EnhAnced Spatial modeling for Expressions Reconstruction), which addresses these challenges and enhances 3D facial geometry performance. TEASER tackles two main limitations of existing methods: insufficient photometric loss for self-reconstruction and inaccurate localization of subtle expressions. We introduce a multi-scale tokenizer to extract facial appearance information. Combined with a neural renderer, these tokens provide precise geometric guidance for expression reconstruction. Furthermore, TEASER incorporates a pose-dependent landmark loss to further improve geometric performances. Our approach not only significantly enhances expression reconstruction quality but also offers interpretable tokens suitable for various downstream applications, such as photorealistic facial video driving, expression transfer, and identity swapping. Quantitative and qualitative experimental results across multiple datasets demonstrate that TEASER achieves state-of-the-art performance in precise expression reconstruction.

Yachun Mi, Yu Li, Weicheng Meng et al.

The rapid growth of long-duration, high-definition videos has made efficient video quality assessment (VQA) a critical challenge. Existing research typically tackles this problem through two main strategies: reducing model parameters and resampling inputs. However, light-weight Convolution Neural Networks (CNN) and Transformers often struggle to balance efficiency with high performance due to the requirement of long-range modeling capabilities. Recently, the state-space model, particularly Mamba, has emerged as a promising alternative, offering linear complexity with respect to sequence length. Meanwhile, efficient VQA heavily depends on resampling long sequences to minimize computational costs, yet current resampling methods are often weak in preserving essential semantic information. In this work, we present MVQA, a Mamba-based model designed for efficient VQA along with a novel Unified Semantic and Distortion Sampling (USDS) approach. USDS combines semantic patch sampling from low-resolution videos and distortion patch sampling from original-resolution videos. The former captures semantically dense regions, while the latter retains critical distortion details. To prevent computation increase from dual inputs, we propose a fusion mechanism using pre-defined masks, enabling a unified sampling strategy that captures both semantic and quality information without additional computational burden. Experiments show that the proposed MVQA, equipped with USDS, achieve comparable performance to state-of-the-art methods while being $2\times$ as fast and requiring only $1/5$ GPU memory.