Despite recent advances in feed-forward 3D Gaussian Splatting, generalizable 3D reconstruction remains challenging, particularly in multi-view correspondence modeling. Existing approaches face a fundamental trade-off: explicit methods achieve geometric precision but struggle with ambiguous regions, while implicit methods provide robustness but suffer from slow convergence. We present H3R, a hybrid framework that addresses this limitation by integrating volumetric latent fusion with attention-based feature aggregation. Our framework consists of two complementary components: an efficient latent volume that enforces geometric consistency through epipolar constraints, and a camera-aware Transformer that leverages Plücker coordinates for adaptive correspondence refinement. By integrating both paradigms, our approach enhances generalization while converging 2$\times$ faster than existing methods. Furthermore, we show that spatial-aligned foundation models (e.g., SD-VAE) substantially outperform semantic-aligned models (e.g., DINOv2), resolving the mismatch between semantic representations and spatial reconstruction requirements. Our method supports variable-number and high-resolution input views while demonstrating robust cross-dataset generalization. Extensive experiments show that our method achieves state-of-the-art performance across multiple benchmarks, with significant PSNR improvements of 0.59 dB, 1.06 dB, and 0.22 dB on the RealEstate10K, ACID, and DTU datasets, respectively. Code is available at https://github.com/JiaHeng-DLUT/H3R.

Video captioning is a challenging task and typically requires paired video-text data for training. However, manually annotating coherent textual descriptions for videos is laborious and time-consuming. To address this challenge, we propose a novel approach that enhances video captioning using only synthetic text data. Leveraging the exceptional text generation capabilities of large language models (LLMs), we produce high-quality and diverse video captions tailored to the target domain. Our approach employs a two-stage prompting strategy: first prompt GPT-4 with few-shot target-domain captions to create a set of high-quality captions, and then continue prompting with the generated captions to acquire large-scale synthetic data. To effectively utilize these captions, we introduce Mixture of Scale and Shift experts (MoS2), an efficient adaptation method for pre-trained captioning models. MoS2 employs lightweight routing networks to estimate probability distributions over a collection of scale and shift experts, dynamically allocating tokens to the appropriate experts. This dynamic adjustment mechanism enhances the model's ability to handle data variations and mitigates the distribution shift between synthetic and real captions. Moreover, our method reduces the number of learnable parameters, facilitating more efficient adaptation. Our method achieves superior performance with only synthetic text data, narrowing the gap between zero-shot and fine-tuned models and reducing the dependency on paired data from the target domain.

We introduce MABe22, a large-scale, multi-agent video and trajectory benchmark to assess the quality of learned behavior representations. This dataset is collected from a variety of biology experiments, and includes triplets of interacting mice (4.7 million frames video+pose tracking data, 10 million frames pose only), symbiotic beetle-ant interactions (10 million frames video data), and groups of interacting flies (4.4 million frames of pose tracking data). Accompanying these data, we introduce a panel of real-life downstream analysis tasks to assess the quality of learned representations by evaluating how well they preserve information about the experimental conditions (e.g. strain, time of day, optogenetic stimulation) and animal behavior. We test multiple state-of-the-art self-supervised video and trajectory representation learning methods to demonstrate the use of our benchmark, revealing that methods developed using human action datasets do not fully translate to animal datasets. We hope that our benchmark and dataset encourage a broader exploration of behavior representation learning methods across species and settings.