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ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions
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ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions
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Editing images with instructions to reflect non-rigid motions, camera viewpoint shifts, object deformations, human articulations, and complex interactions, poses a challenging yet underexplored problem in computer vision. Existing approaches and datasets predominantly focus on static scenes or rigid transformations, limiting their capacity to handle expressive edits involving dynamic motion. To address this gap, we introduce ByteMorph, a comprehensive framework for instruction-based image editing with an emphasis on non-rigid motions. ByteMorph comprises a large-scale dataset, ByteMorph-6M, and a strong baseline model built upon the Diffusion Transformer (DiT), named ByteMorpher. ByteMorph-6M includes over 6 million high-resolution image editing pairs for training, along with a carefully curated evaluation benchmark ByteMorph-Bench. Both capture a wide variety of non-rigid motion types across diverse environments, human figures, and object categories. The dataset is constructed using motion-guided data generation, layered compositing techniques, and automated captioning to ensure diversity, realism, and semantic coherence. We further conduct a comprehensive evaluation of recent instruction-based image editing methods from both academic and commercial domains.
Forward citations
Cited by 13 Pith papers
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CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences
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WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing
WeEdit trains a glyph-guided, RL-optimized image editor on a 330K-pair synthetic multilingual dataset and reports open-source SOTA on its own bilingual and multilingual text-editing benchmarks.
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Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework
Introduces HOI-Edit benchmark with HOI-Eval metric and SCPE self-correcting framework leveraging I2V models for competitive HOI image editing performance.
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Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework
HOI-Edit benchmarks human–object interaction image editing with a VLM-based metric, and SCPE steers I2V models via agentic self-correcting prompts to produce competitive HOI edits.
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SIGMA: Semantic-Difference Instruction-Grounding Mask Annotator for Text-Driven Image Manipulation Localization
SIGMA generates accurate IML masks via semantic feature differencing and instruction-guided cross-modal refinement, yielding a 1.1M training set that boosts six detectors by 18.34% F1 on five datasets.
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DDA-Thinker: Decoupled Dual-Atomic Reinforcement Learning for Reasoning-Driven Image Editing
DDA-Thinker decouples planning from generation and applies dual-atomic RL with checklist-based rewards to boost reasoning in image editing, yielding competitive results on RISE-Bench and KRIS-Bench.
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LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing
LIVE achieves state-of-the-art instruction-based video editing by jointly training on image and video data with a frame-wise token noise strategy to bridge domain gaps and a new benchmark of over 60 tasks.
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EditCaption: Human-Refined SFT and HAE-DPO for Image Editing Instruction Synthesis
EditCaption reduces critical errors in automated image editing instructions from 47.75% to 23% via SFT and DPO, yielding fine-tuned models that match or exceed closed-source VLMs on Eval-400 and ByteMorph-Bench.
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EditCaption: Human-Refined SFT and HAE-DPO for Image Editing Instruction Synthesis
A 235B VLM trained with human-refined SFT and hardness-adaptive error-aware DPO cuts critical instruction errors from ~48% to ~18% and beats Gemini-3-Pro on three editing-instruction benchmarks.
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PhyEdit: Towards Real-World Object Manipulation via Physically-Grounded Image Editing
PhyEdit improves physical accuracy in image object manipulation by using explicit geometric simulation as 3D-aware guidance combined with joint 2D-3D supervision.
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Towards Robust Sequential Decomposition for Complex Image Editing
Sequential decomposition trained on synthetic editing tasks improves robustness for complex image instructions and transfers to real images via co-training.
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Instruction-based Image Editing: A Survey on Data, Models, Evaluation, and Applications
A survey of instruction-based image editing plus a new 21-task benchmark, CDD-IIE, on which ten open models are scored by human experts.
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Towards Robust Sequential Decomposition for Complex Image Editing
Develops a synthetic data pipeline for training sequential decomposition in generative image editing, showing robust gains with complexity and sim-to-real transfer via co-training.
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