Good Papers

Showing Image editing Show all papers

45%Niche pick
?Niche pickVote to see the score

EditDistill: Is It Possible to Guide Video Editing with Image Editing

guojun lei, Hong Li, Hongbing Yang, Lixue Gong and 2 more

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

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

Stroke-Audited Quadratic Bezier Splatting with Structural Initialization for Efficient Painting Rendering

Jinfan Liu, Xuhan Zhan, Mengqin Zhao, Guangyi Deng and 2 more

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

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

Identifying and Mitigating Diversity Collapse in Zero-Shot Personalization with I2I Editing Models

Uichan Lee, Jongeon Baek, Sangheum Hwang

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

StatLUT: Statistical Feature-Driven Multimodal 3D LUT Generation for Photorealistic Style Transfer

Yifan Wang, Zhixiang Hao, Yu Wang, Congchao Zhu

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

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score

G2Fusion: Geometric-to-Generative Image Fusion via Registration-Restoration Evolution

Hao Zhang, Douyu Wu, Han Xu, Linfeng Tang and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

GMO-E²DIT: Grounded Multi-Operation Editing for E-Commerce Images

Zipeng Guo, Xiaoan Liu, Lichen Ma, Cheng Wang and 8 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

FlashRelight: Portrait Video Relighting with Dynamic Lighting

Jiahui Sheng, Le Li, Limin Lin, Jiahao Li and 5 more

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

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score

LOCO: Local Light-Aware Object Compositing with Spatially Varying Illumination-Augmented Data

Jinseo Jeong, Hyunsoo Kim, Junseo Koo, Junhyeog Yun and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
78%Highly rated
?Highly ratedVote to see the score

CLeaR: A Unified Framework for Resolving the Leakage–Degradation Dilemma in Style Transfer

CLeaR resolves style transfer's leakage-degradation dilemma via orthogonal subspace projection, ensemble inversion, and energy-guided diffusion calibration to cut content leakage and improve style fidelity.

Teng Zhou, Yunhao Chen

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 0/5
72%Highly rated
?Highly ratedVote to see the score

SyncLight: Single-Edit Multi-View Relighting

SyncLight propagates single-view lighting edits across arbitrary uncalibrated multi-view captures via a latent bridge-matched diffusion transformer, enabling consistent high-fidelity relighting without camera poses.

David Serrano-Lozano, Anand Bhattad, Luis Herranz, Jean-Francois Lalonde and 1 more

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
8/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 8 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 0/5
74%Highly rated
?Highly ratedVote to see the score

ProEdit: Inversion-based Editing From Prompts Done Right

ProEdit improves inversion-based image and video editing via KV-mix and Latents-Shift to achieve state-of-the-art results.

Zhi Ouyang, Dian Zheng, Xiao-Ming Wu, Jian-Jian Jiang and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 19 on Hugging Face · Code ★ 116

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 3/5
medium 6/10
strict 0/5
83%Must read
?Must readVote to see the score

GenScale: A Benchmark for Relative Object Scale in Image Generation and Editing

GenScale benchmarks relative object scale in image generation and editing, finding current models unreliable, while Rescale improves scale plausibility via localized correction.

Lingxiao Li, Max Whitton, Ledell Wu, Boqing Gong

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 1/5
88%Must read
?Must readVote to see the score

Inline Critic Steers Image Editing

Inline Critic uses learnable tokens to critique frozen image-editing models at intermediate layers, steering hidden states during the forward pass to achieve state-of-the-art results.

Weitai Kang, Xiaohang Zhan, Yizhou Wang, Mang Tik Chiu and 3 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
15/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 2/5
80%Must read
?Must readVote to see the score

EditBridge: Towards Faithful and Efficient Ultra-High-Resolution Image Editing

EditBridge formulates ultra-high-resolution editing as structured low-to-high translation conditioned on original sources to prevent divergence and texture degradation, achieving 4K editing with 3.6-8.4x speedups via sparse attention.

Jiayi Song, Shijie Huang, Fangtai Wu, Yubo Huang and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 23 on Hugging Face · Code ★ 23

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5
74%Highly rated
?Highly ratedVote to see the score

Edit the Bits, Diff the Codes: Bitwise Residual Editing for Visual Autoregressive Models

BitResEdit guides bitwise-residual VAR generators via bit-level source-negative guidance and masked multi-scale residual code injection for localized edits with preserved backgrounds.

Shengqiang Zhang, Ruotong Liao, Volker Tresp, Barbara Plank and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 2/5
medium 7/10
strict 0/5
78%Highly rated
?Highly ratedVote to see the score

Thinking in Boxes: 3D Editing in Real Images Made Easy

The method treats 3D box pairs as structured transformation specs for precise real-image editing, outperforming state-of-the-art on large 3D edits.

Pradhaan Bhat, Naveen Chandra R, Rishubh Parihar, Vaibhav Vavilala and 3 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
72%Highly rated
?Highly ratedVote to see the score

RoPEMover: Depth-Aware Object Relocation via Positional Embeddings

RoPEMover manipulates diffusion transformer position embeddings to move objects with depth-aware 3D geometry, preserving identity, occlusions, and shadows with minimal real data.

Ipek Oztas, Duygu Ceylan, Aybars B Aksoy, Aysegul Dundar

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
8/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 8 of 20 reviewers recommend it
lenient 4/5
medium 4/10
strict 0/5