PhysRemover: A Unified Framework for Physically Realistic Object Removal

While diffusion models have revolutionized image generation and editing, achieving physically realistic object removal remains a challenge. Physically realistic object removal posits that if an object ceases to exist, all its directly associated transient physical effects must also vanish. However, existing methods either confine the removal strictly to the masked region or limit "side effects" to simple shadows and reflections, ignoring complex interactions like caustics, emitted light, and volumetric media. Furthermore, they struggle with transparent objects, destroying the underlying background semantics. To bridge these gaps, we formalize the task of physically realistic object removal and propose PhysRemover, a unified framework addressing both the elimination of objects with their physical effects and the handling of transparent materials. Specifically, we incorporate an In-Context Contrastive Guidance mechanism guided by both masked and unmasked image, coupled with a Learnable Removal Trigger. This design effectively resolves the conflict between erasing the object and its physical side effects, and preserving the underlying background. Furthermore, we introduce PhyTrace, a large-scale image dataset capturing diverse physical effects, along with two benchmarks tailored for the proposed task of physically realistic object removal. Additionally, we develop a novel metric, PhysRM, for evaluating object removal quality, establishing a new standard for physically realistic image editing. Extensive experiments demonstrate that PhysRemover effectively eliminates objects and their physical side effects while faithfully preserving the background when removing transparent objects, outperforming existing methods in these challenging settings.