PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling

High-quality human reconstruction and photo-realistic rendering of a dynamic scene is a long-standing problem in computer vision and graphics. Despite considerable efforts invested in developing various capture systems and reconstruction algorithms, recent advancements still struggle with loose or oversized clothing and overly complex poses. In part, this is due to the challenges of acquiring high-quality human datasets. To facilitate the development of these fields, in this paper, we present PKU-DyMVHumans, a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos. It comprises 8.2 million frames captured by more than 56 synchronized cameras across diverse scenarios. These sequences comprise 32 human subjects across 45 different scenarios, each with a high-detailed appearance and realistic human motion. Inspired by recent advancements in neural radiance field (NeRF)-based scene representations, we carefully set up an off-the-shelf framework that is easy to provide those state-of-the-art NeRF-based implementations and benchmark on PKU-DyMVHumans dataset. It is paving the way for various applications like fine-grained foreground/background decomposition, high-quality human reconstruction and photo-realistic novel view synthesis of a dynamic scene. Extensive studies are performed on the benchmark, demonstrating new observations and challenges that emerge from using such high-fidelity dynamic data.

UCVC: A Unified Contextual Video Compression Framework with Joint P-frame and B-frame Coding

This paper presents a learned video compression method in response to video compression track of the 6th Challenge on Learned Image Compression (CLIC), at DCC 2024.Specifically, we propose a unified contextual video compression framework (UCVC) for joint P-frame and B-frame coding. Each non-intra frame refers to two neighboring decoded frames, which can be either both from the past for P-frame compression, or one from the past and one from the future for B-frame compression. In training stage, the model parameters are jointly optimized with both P-frames and B-frames. Benefiting from the designs, the framework can support both P-frame and B-frame coding and achieve comparable compression efficiency with that specifically designed for P-frame or B-frame.As for challenge submission, we report the optimal compression efficiency by selecting appropriate frame types for each test sequence. Our team name is PKUSZ-LVC.

Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics

Automatic image colorization is inherently an ill-posed problem with uncertainty, which requires an accurate semantic understanding of scenes to estimate reasonable colors for grayscale images. Although recent interaction-based methods have achieved impressive performance, it is still a very difficult task to infer realistic and accurate colors for automatic colorization. To reduce the difficulty of semantic understanding of grayscale scenes, this paper tries to utilize corresponding audio, which naturally contains extra semantic information about the same scene. Specifically, a novel audio-infused automatic image colorization (AIAIC) network is proposed, which consists of three stages. First, we take color image semantics as a bridge and pretrain a colorization network guided by color image semantics. Second, the natural co-occurrence of audio and video is utilized to learn the color semantic correlations between audio and visual scenes. Third, the implicit audio semantic representation is fed into the pretrained network to finally realize the audio-guided colorization. The whole process is trained in a self-supervised manner without human annotation. In addition, an audiovisual colorization dataset is established for training and testing. Experiments demonstrate that audio guidance can effectively improve the performance of automatic colorization, especially for some scenes that are difficult to understand only from visual modality.

Learned Image Compression with ROI-Weighted Distortion and Bit Allocation

This one page paper describes our method for the track of image compression. To achieve better perceptual quality, we use the adversarial loss to generate realistic textures, use region of interest (ROI) mask to guide the bit allocation for different regions. Our Team name is TLIC.

MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression

Recently, multi-reference entropy model has been proposed, which captures channel-wise, local spatial, and global spatial correlations. Previous works adopt attention for global correlation capturing, however, the quadratic cpmplexity limits the potential of high-resolution image coding. In this paper, we propose the linear complexity global correlations capturing, via the decomposition of softmax operation. Based on it, we propose the MLIC$^{++}$, a learned image compression with linear complexity for multi-reference entropy modeling. Our MLIC$^{++}$ is more efficient and it reduces BD-rate by 12.44% on the Kodak dataset compared to VTM-17.0 when measured in PSNR. Code will be available at https://github.com/JiangWeibeta/MLIC.

HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform

In recent years, there has been rapid development in learned image compression techniques that prioritize ratedistortion-perceptual compression, preserving fine details even at lower bit-rates. However, current learning-based image compression methods often sacrifice human-friendly compression and require long decoding times. In this paper, we propose enhancements to the backbone network and loss function of existing image compression model, focusing on improving human perception and efficiency. Our proposed approach achieves competitive subjective results compared to state-of-the-art end-to-end learned image compression methods and classic methods, while requiring less decoding time and offering human-friendly compression. Through empirical evaluation, we demonstrate the effectiveness of our proposed method in achieving outstanding performance, with more than 25% bit-rate saving at the same subjective quality.

SLIC: Self-Conditioned Adaptive Transform with Large-Scale Receptive Fields for Learned Image Compression

Learned image compression has achieved remarkable performance. Transform, plays an important role in boosting the RD performance. Analysis transform converts the input image to a compact latent representation. The more compact the latent representation is, the fewer bits we need to compress it. When designing better transform, some previous works adopt Swin-Transformer. The success of the Swin-Transformer in image compression can be attributed to the dynamic weights and large receptive field.However,the LayerNorm adopted in transformers is not suitable for image compression.We find CNN-based modules can also be dynamic and have large receptive-fields. The CNN-based modules can also work with GDN/IGDN. To make the CNN-based modules dynamic, we generate the weights of kernels conditioned on the input feature. We scale up the size of each kernel for larger receptive fields. To reduce complexity, we make the CNN-module channel-wise connected. We call this module Dynamic Depth-wise convolution. We replace the self-attention module with the proposed Dynamic Depth-wise convolution, replace the embedding layer with a depth-wise residual bottleneck for non-linearity and replace the FFN layer with an inverted residual bottleneck for more interactions in the spatial domain. The interactions among channels of dynamic depth-wise convolution are limited. We design the other block, which replaces the dynamic depth-wise convolution with channel attention. We equip the proposed modules in the analysis and synthesis transform and receive a more compact latent representation and propose the learned image compression model SLIC, meaning Self-Conditioned Adaptive Transform with Large-Scale Receptive Fields for Learned Image Compression Learned Image Compression. Thanks to the proposed transform modules, our proposed SLIC achieves 6.35% BD-rate reduction over VVC when measured in PSNR on Kodak dataset.

Butterfly: Multiple Reference Frames Feature Propagation Mechanism for Neural Video Compression

Using more reference frames can significantly improve the compression efficiency in neural video compression. However, in low-latency scenarios, most existing neural video compression frameworks usually use the previous one frame as reference. Or a few frameworks which use the previous multiple frames as reference only adopt a simple multi-reference frames propagation mechanism. In this paper, we present a more reasonable multi-reference frames propagation mechanism for neural video compression, called butterfly multi-reference frame propagation mechanism (Butterfly), which allows a more effective feature fusion of multi-reference frames. By this, we can generate more accurate temporal context conditional prior for Contextual Coding Module. Besides, when the number of decoded frames does not meet the required number of reference frames, we duplicate the nearest reference frame to achieve the requirement, which is better than duplicating the furthest one. Experiment results show that our method can significantly outperform the previous state-of-the-art (SOTA), and our neural codec can achieve -7.6% bitrate save on HEVC Class D dataset when compares with our base single-reference frame model with the same compression configuration.