2025/06/12 by Bolin Chen, Ru-Ling Liao, Chen, Bolin +5 · 1 citation
Computer Science · #Advanced Data Compression Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Image and Video Processing (eess.IV) #Video Coding and Compression Technologies #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2506.10453
openalex publication_date 2025/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Beyond traditional hybrid-based video codec, generative video codec could achieve promising compression performance by evolving high-dimensional signals into compact feature representations for bitstream compactness at the encoder side and developing explicit motion fields as intermediate supervision for high-quality reconstruction at the decoder side. This paradigm has achieved significant success in face video compression. However, compared to facial videos, human body videos pose greater challenges due to their more complex and diverse motion patterns, i.e., when using explicit motion guidance for Generative Human Video Coding (GHVC), the reconstruction results could suffer severe distortions and inaccurate motion. As such, this paper highlights the limitations of explicit motion-based approaches for human body video compression and investigates the GHVC performance improvement with the aid of Implicit Motion Transformation, namely IMT. In particular, we propose to characterize complex human body signal into compact visual features and transform these features into implicit motion guidance for signal reconstruction. Experimental results demonstrate the effectiveness of the proposed IMT paradigm, which can facilitate GHVC to achieve high-efficiency compression and high-fidelity synthesis.