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From a small amount of real data to diverse training data: lighting transfer, object removal/replacement, EGO-view synthesis and pixel-level auto annotation.
Multi-light environment transfer (stage light, window light) with physical consistency.
Precise removal of objects (eraser, apple, bottle cap); apple-to-lemon, Rubik's-cube-to-sponge replacement.
Add or remove objects in EGO view — peelers and plastic bags removed while preserving physics; phones and drinks naturally blended into scenes.
Hand-pose parsing and precise reconstruction for manipulation-level synthesis.
Automatic generation of instance segmentation, hand pose, depth, optical flow and object trajectory annotations.
High-speed physics-model inference quickly generates high-fidelity synthetic samples from real data.
Built on the EditReal original synthesis paradigm, starting from real collected data and combining lighting transfer, object add/remove/replace, EGO-view synthesis and pixel-level auto annotation to produce high-fidelity annotated synthetic data at scale, cutting collection costs and shortening model iteration cycles.
Built on the EditReal original synthesis paradigm, starting from real collected data and combining lighting transfer, object add/remove/replace, EGO-view synthesis and pixel-level auto annotation to produce high-fidelity annotated synthetic data at scale, cutting collection costs and shortening model iteration cycles.