Best of CVPR

Reconstructing 3D face models from a single image is an inherently ill-posed problem, which becomes even more challenging in the presence of occlusions where multiple reconstructions can be equally valid. Despite the ubiquity of the problem, very few methods address its multi-hypothesis nature. In this paper we introduce OFER, a novel approach for single-image 3D face reconstruction that can generate plausible, diverse, and expressive 3D faces by training two diffusion models to generate a shape and expression coefficients of face parametric model, conditioned on the input image. To maintain consistency across diverse expressions, the challenge is to select the best matching shape. To achieve this, we propose a novel ranking mechanism that sorts the outputs of the shape diffusion network based on predicted shape accuracy scores.

Video anomaly detection is crucial for ensuring safety and security, yet existing benchmarks overlook the unique context of smart home environments. We introduce SmartHome-Bench, a dataset of 1,203 smart home videos annotated according to a novel taxonomy of seven anomaly categories, such as Wildlife, Senior Care, and Baby Monitoring. We evaluate state-of-the-art closed- and open-source multimodal LLMs with various prompting techniques, revealing significant performance gaps. To address these limitations, we propose the Taxonomy-Driven Reflective LLM Chain (TRLC), which boosts detection accuracy by 11.62%.