Title: From Frames to Sequences: Temporally Consistent Human-Centric Dense Prediction
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Dense prediction, Depth, Surface normal

Abstract: In this work, we focus on the challenge of temporally consistent human-centric dense prediction across video sequences. While progress has been made in per-frame predictions of depth, surface normals, and segmentation, achieving stability under motion, occlusion, and illumination changes remains difficult. For this, we design a synthetic data pipeline that produces large-scale photorealistic human images and motion-aligned video sequences with high-fidelity annotations. Unlike prior static data synthetic pipelines, our pipeline provides both frame-level and sequence-level supervision, supporting the learning of spatial accuracy and temporal stability. Building on this, we introduce a model that integrates human-centric priors and temporal modules to jointly estimate temporally consistent segmentation, depth, and surface normals within a single framework. Our two-stage training strategy, combining static pretraining with dynamic sequence supervision, enables the model to first acquire robust spatial representations and then refine temporal consistency across motion-aligned sequences. Extensive experiments show that we achieve state-of-the-art performance on THuman2.1 and Hi4D and generalize effectively to in-the-wild videos.