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Abudouaihati Batuer

dblp:407/9735 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Virtual and augmented reality · 30% Rendering · 30% Image and video coding · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering › gaussian splatting
4d gaussian splatting
0.912025
GIFStream: 4D Gaussian-based Immersive Video with Feature Stream · CVPR 2025
Virtual and augmented reality
immersive video
0.912025
GIFStream: 4D Gaussian-based Immersive Video with Feature Stream · CVPR 2025
Image and video coding
video compression
0.912025
GIFStream: 4D Gaussian-based Immersive Video with Feature Stream · CVPR 2025
Geometric modeling and processing › deformation modeling
deformation field
0.312025
GIFStream: 4D Gaussian-based Immersive Video with Feature Stream · CVPR 2025

Methods — techniques the papers use, named apart from their topics

time-dependent feature stream · 0.9temporal and spatial compression · 0.94d gaussian splatting · 0.9
YearPublicationVenuePosition
2025 GIFStream: 4D Gaussian-based Immersive Video with Feature Stream
abstract
Immersive video offers a 6-Dof-Free viewing experience, potentially playing a key role in future video technology. Recently, 4D Gaussian Splatting has gained attention as an effective approach for immersive video due to its high rendering efficiency and quality, though maintaining quality with manageable storage remains challenging. To address this, we introduce GIFStream, a novel 4D Gaussian representation using a canonical space and a deformation field enhanced with time-dependent feature streams. These feature streams enable complex motion modeling and allow efficient compression by leveraging their motion-awareness and temporal correspondence. Additionally, we incorporate both temporal and spatial compression networks for end-to-end compression. Experimental results show that GIFStream delivers high-quality immersive video at 30 Mbps, with real-time rendering and fast decoding on an RTX 4090.
Hao Li 0069, Sicheng Li 0003, Abudouaihati Batuer, Lu Yu 0003, Yiyi Liao
CVPR4