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57%Worth a look
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Pixel-space Autoregressive Image Synthesis via Spectrum Serialization and Flow-based Refinement

Guiwei Zhang, Tianyu Zhang, Yalong Bai, Ying Ba and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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Learning Preference Representations for Preference-Conditioned Image Generation

Wenyi Mo, Tianyu Zhang, Yalong Bai, Ligong Han and 2 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
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PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation

Yanjie Pan, Qingdong He, Zhengkai Jiang, Pengcheng Xu and 8 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
78%Highly rated
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Breaking the Uniformity Trap: Scaling Video Diffusion Model via SplitMoE

SplitMoE replaces uniform token-wise routing with split semantic and generic experts, improving video diffusion convergence, routing coherence, and generation quality over load-balanced MoEs.

Yu Xu, Yuxin Zhang, Xiao Yang, Haotian Yang and 6 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 6 on Hugging Face

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 0/5
76%Highly rated
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Detect Anything in Graphic Design: Element-Level Rewards for Autoregressive Detection

DAD treats graphic design detection as ordered compositional deconstruction with amodal bounding boxes and element-level reinforcement learning, achieving human-level amodal detection and outperforming baselines across nine benchmarks.

Jiangning Zhu, Bowen Li, Shenyu Qiao, Yima Gu and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 1/5
89%Must read
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PixelART: Image-to-Layer Decomposition without Latents or Text-to-Image Pretraining

PixelART trains a pixel-space diffusion transformer from scratch to decompose images into editable RGBA layers, achieving state-of-the-art results with 80% fewer parameters and 98% lower latency than pretrained alternatives.

Zelin Jia, Zhao Zhang, Zhicong Tang, Yuhui Yuan and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5