arXiv subjects

Anton Sochenov

Publications and source records attributed to Anton Sochenov.

4 recordsLinked to original sources

arXiv2025

CGVQM+D: Computer Graphics Video Quality Metric and Dataset

While existing video and image quality datasets have extensively studied natural videos and traditional distortions, the perception of synthetic content and modern rendering artifacts remains underexplored. We present a novel video quality dataset focused on distortions introduced by advanced rendering techniques, including neural supersampling, novel-view synthesis, path tracing, neural denoising, frame interpolation, and variable rate shading. Our evaluations show that existing full-reference quality metrics perform sub-optimally on these distortions, with a maximum Pearson correlation of 0.78. Additionally, we find that the feature space of pre-trained 3D CNNs aligns strongly with human perception of visual quality. We propose CGVQM, a full-reference video quality metric that significantly outperforms existing metrics while generating both per-pixel error maps and global quality scores. Our dataset and metric implementation is available at https://github.com/IntelLabs/CGVQM.

cs.GR↗

arXiv2025

Image-GS: Content-Adaptive Image Representation via 2D Gaussians

Neural image representations have emerged as a promising approach for encoding and rendering visual data. Combined with learning-based workflows, they demonstrate impressive trade-offs between visual fidelity and memory footprint. Existing methods in this domain, however, often rely on fixed data structures that suboptimally allocate memory or compute-intensive implicit models, hindering their practicality for real-time graphics applications. Inspired by recent advancements in radiance field rendering, we introduce Image-GS, a content-adaptive image representation based on 2D Gaussians. Leveraging a custom differentiable renderer, Image-GS reconstructs images by adaptively allocating and progressively optimizing a group of anisotropic, colored 2D Gaussians. It achieves a favorable balance between visual fidelity and memory efficiency across a variety of stylized images frequently seen in graphics workflows, especially for those showing non-uniformly distributed features and in low-bitrate regimes. Moreover, it supports hardware-friendly rapid random access for real-time usage, requiring only 0.3K MACs to decode a pixel. Through error-guided progressive optimization, Image-GS naturally constructs a smooth level-of-detail hierarchy. We demonstrate its versatility with several applications, including texture compression, semantics-aware compression, and joint image compression and restoration.

cs.CV↗

arXiv2024

Hybrid Rendering for Dynamic Scenes

Despite significant advances in algorithms and hardware, global illumination continues to be a challenge in the real-time domain. Time constraints often force developers to either compromise on the quality of global illumination or disregard it altogether. We take advantage of a common setup in modern games: having a set of a level, which is a static scene with dynamic characters and lighting. We introduce a novel method for efficiently and accurately rendering global illumination in dynamic scenes. Our hybrid technique leverages precomputation and neural networks to capture the light transport of a static scene. Then, we introduce a method to compute the difference between the current scene and the static scene, which we already precomputed. By handling the bulk of the light transport through precomputation, our method only requires the rendering of a minimal difference, reducing the noise and increasing the quality.

cs.GR↗

arXiv2024

GFFE: G-buffer Free Frame Extrapolation for Low-latency Real-time Rendering

Real-time rendering has been embracing ever-demanding effects, such as ray tracing. However, rendering such effects in high resolution and high frame rate remains challenging. Frame extrapolation methods, which don't introduce additional latency as opposed to frame interpolation methods such as DLSS 3 and FSR 3, boost the frame rate by generating future frames based on previous frames. However, it is a more challenging task because of the lack of information in the disocclusion regions, and recent methods also have a high engine integration cost due to requiring G-buffers as input. We propose a \emph{G-buffer free} frame extrapolation, GFFE, with a novel heuristic framework and an efficient neural network, to plausibly generate new frames in real-time without introducing additional latency. We analyze the motion of dynamic fragments and different types of disocclusions, and design the corresponding modules of the extrapolation block to handle them. After filling disocclusions, a light-weight shading correction network is used to correct shading and improve overall quality. GFFE achieves comparable or better results compared to previous interpolation as well as G-buffer-dependent extrapolation methods, with more efficient performance and easier game integration.

cs.CV↗