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Jiacong Chen

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EAAI Journal 2026 Journal Article

Progressive information integration in lightweight image super-resolution

Longfeng Shen
Jiacong Chen
Liangjin Diao
Lei Liu
Fenglan Qin
Fangzhen Ge

Transformer-based models have considerably improved image super-resolution (SR). However, these networks fail to design multi-operations aggregation architecture with guidance of SR knowledge. To tackle these drawbacks, we propose a novel progressive information integration (PII) module. It extracts the input features from progressive perspectives: a dense region, sparse region, and three-dimensional space. We employ a local convolution block to access pixels in the dense region and window-based self-attention for those in the sparse region. To leverage the advantages of both channel-attention and spatial-attention schemes, we introduce a Hybrid Attention block (HAB). This block enables the effective use of pixels in three-dimensional space by combining the complementary benefits of the two attention schemes. As an artificial intelligence (AI) -driven approach, our method is applied to lightweight image super-resolution tasks, aiming to balance performance and computational efficiency. Extensive experiments demonstrate that our PII-based super-resolution (PII-SR) achieves the superior results on lightweight SR benchmarks with fewer parameters (e. g. , 26. 81 dB (dB)@Urban100 × 4 with only 652 thousand (K) parameters).

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NeurIPS Conference 2025 Conference Paper

Motion Matters: Compact Gaussian Streaming for Free-Viewpoint Video Reconstruction

Jiacong Chen
Qingyu Mao
Youneng Bao
Xiandong MENG
Fanyang Meng
Ronggang Wang
Yongsheng Liang

3D Gaussian Splatting (3DGS) has emerged as a high-fidelity and efficient paradigm for online free-viewpoint video (FVV) reconstruction, offering viewers rapid responsiveness and immersive experiences. However, existing online methods face challenge in prohibitive storage requirements primarily due to point-wise modeling that fails to exploit the motion properties. To address this limitation, we propose a novel Compact Gaussian Streaming (ComGS) framework, leveraging the locality and consistency of motion in dynamic scene, that models object-consistent Gaussian point motion through keypoint-driven motion representation. By transmitting only the keypoint attributes, this framework provides a more storage-efficient solution. Specifically, we first identify a sparse set of motion-sensitive keypoints localized within motion regions using a viewspace gradient difference strategy. Equipped with these keypoints, we propose an adaptive motion-driven mechanism that predicts a spatial influence field for propagating keypoint motion to neighboring Gaussian points with similar motion. Moreover, ComGS adopts an error-aware correction strategy for key frame reconstruction that selectively refines erroneous regions and mitigates error accumulation without unnecessary overhead. Overall, ComGS achieves a remarkable storage reduction of over 159 × compared to 3DGStream and 14 × compared to the SOTA method QUEEN, while maintaining competitive visual fidelity and rendering speed. Project page: https: //chenjiacong-1005. github. io/ComGS/.

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