Generative face video coding (GFVC) has been demonstrated as a potential approach to low-latency, low bitrate video conferencing. GFVC frameworks achieve an extreme gain in coding efficiency with over 70% bitrate savings when compared to conventional codecs at bitrates below 10kbps. In recent MPEG/JVET standardization efforts, all the information required to reconstruct video sequences using GFVC frameworks are adopted as part of the supplemental enhancement information (SEI) in existing compression pipelines. In light of this development, we aim to address a challenge that has been weakly addressed in prior GFVC frameworks, i.e., reconstruction drift as the distance between the reference and target frames increases. This challenge creates the need to update the reference buffer more frequently by transmitting more Intra-refresh frames, which are the most expensive element of the GFVC bitstream. To overcome this problem, we propose instead multiple reference animation as a robust approach to minimizing reconstruction drift, especially when used in a bi-directional prediction mode. Further, we propose a contrastive learning formulation for multi-reference animation. We observe that using a contrastive learning framework enhances the representation capabilities of the animation generator. The resulting framework, MRDAC (Multi-Reference Deep Animation Codec) can therefore be used to compress longer sequences with fewer reference frames or achieve a significant gain in reconstruction accuracy at comparable bitrates to previous frameworks. Quantitative and qualitative results show significant coding and reconstruction quality gains compared to previous GFVC methods, and more accurate animation quality in presence of large pose and facial expression changes. The source code will be available at https://github.com/Goluck-Konuko/animation-based-codecs

Generative face video coding (GFVC) schemes applied to talking head videos have recently demonstrated significant coding gains compared to traditional coding frameworks, particularly at ultra-low bitrates. Despite advancements in the field, these methods still face challenges in handling large pose and facial expression changes, as well as (dis-)occlusions. Recently, a hybrid approach (HDAC+) that combines a low-quality video coded with a conventional codec and animation-based coding has been proposed for standardization and shown to partially address these issues. Although HDAC+ shows promising results, it still struggles with generating accurate images. In this paper, we propose HDAC-HF, an improvement to the reconstruction process in HDAC+. Based on empirical observations that some pose and expression details are lost during animation, we introduce a high-frequency (HF) shuttling mechanism to enhance reconstruction fidelity at the decoder side, inspired by recent advancements in video super-resolution. By enhancing the flow of high-frequency details in the feature domain, we improve the reconstruction of facial expressions and poses. Qualitative and quantitative experiments confirm that the proposed method improves reconstruction without any additional bitstream or signaling cost compared to the baseline HDAC+ codec.

We address the problem of efficiently compressing video for conferencing-type applications. We build on recent approaches based on image animation, which can achieve good reconstruction quality at very low bitrate by representing face motions with a compact set of sparse keypoints. However, these methods encode video in a frame-by-frame fashion, i.e., each frame is reconstructed from a reference frame, which limits the reconstruction quality when the bandwidth is larger. Instead, we propose a predictive coding scheme which uses image animation as a predictor, and codes the residual with respect to the actual target frame. The residuals can be in turn coded in a predictive manner, thus removing efficiently temporal dependencies. Our experiments indicate a significant bitrate gain, in excess of 70% compared to the HEVC video standard and over 30% compared to VVC, on a dataset of talking-head videos.

Deep generative models, and particularly facial animation schemes, can be used in video conferencing applications to efficiently compress a video through a sparse set of key-points, without the need to transmit dense motion vectors. While these schemes bring significant coding gains over con-ventional video codecs at low bitrates, their performance saturates quickly when the available bandwidth increases. In this paper, we propose a layered, hybrid coding scheme to overcome this limitation. Specifically, we extend a codec based on facial animation by adding an auxiliary stream con-sisting of a very low bitrate version of the video, obtained through a conventional video codec (e.g., HEVC). The an-imated and auxiliary videos are combined through a novel fusion module. Our results show consistent average BD-Rate gains in excess of -30% on a large dataset of video confer-encing sequences, extending the operational range of bitrates of a facial animation codec alone. Our code is available at github.com/animation-based-codecs

In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep neural networks to encode motion information as keypoint displacement and reconstruct the video signal at the decoder side. The overall system is trained in an end-to-end fashion minimizing a reconstruction error on the encoder output. Objective and subjective quality evaluation experiments demonstrate that the proposed approach provides an average bitrate reduction for the same visual quality of more than 60% compared to HEVC.

In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep neural networks to encode motion information as keypoint displacement and reconstruct the video signal at the decoder side. The overall system is trained in an end-to-end fashion minimizing a reconstruction error on the encoder output. Objective and subjective quality evaluation experiments demonstrate that the proposed approach provides an average bitrate reduction for the same visual quality of more than 80% compared to HEVC.