Tianjia Shao

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We present an interactive approach to semantic modeling of indoor scenes with a consumer-level RGBD camera. Using our approach, the user first takes an RGBD image of an indoor scene, which is automatically segmented into a set of regions with semantic labels. If the segmentation is not satisfactory, the user can draw some strokes to guide the algorithm to(More)
Human motions are the product of internal and external forces, but these forces are very difficult to measure in a general setting. Given a motion capture trajectory, we propose a method to reconstruct its open-loop control and the implicit contact forces. The method employs a strategy based on randomized sampling of the control within user-specified(More)
We propose a sketch-based 3D shape retrieval system that is substantially more discriminative and robust than existing systems, especially for complex models. The power of our system comes from a combination of a contourbased 2D shape representation and a robust sampling-based shape matching scheme. They are defined over discriminative local features and(More)
Missing data due to occlusion is a key challenge in 3D acquisition, particularly in cluttered man-made scenes. Such partial information about the scenes limits our ability to analyze and understand them. In this work we abstract such environments as collections of cuboids and hallucinate geometry in the occluded regions by globally analyzing the physical(More)
We present a novel image-based representation for dynamic 3D avatars, which allows effective handling of various hairstyles and headwear, and can generate expressive facial animations with fine-scale details in real-time. We develop algorithms for creating an image-based avatar from a set of sparsely captured images of a user, using an off-the-shelf web(More)
Concept sketches are popularly used by designers to convey pose and function of products. Understanding such sketches, however, requires special skills to form a mental 3D representation of the product geometry by linking parts across the different sketches and imagining the intermediate object configurations. Hence, the sketches can remain inaccessible to(More)
We introduce <i>AutoHair</i>, the first fully automatic method for 3D hair modeling from a single portrait image, with no user interaction or parameter tuning. Our method efficiently generates complete and high-quality hair geometries, which are comparable to those generated by the state-of-the-art methods, where user interaction is required. The core(More)
We present a technique for parsing widely used furniture assembly instructions, and reconstructing the 3D models of furniture components and their dynamic assembly process. Our technique takes as input a multi-step assembly instruction in a vector graphic format and starts to group the vector graphic primitives into semantic elements representing individual(More)
Inferring the functionality of an object from a single RGBD image is difficult for two reasons: lack of semantic information about the object, and missing data due to occlusion. In this paper, we present an interactive framework to recover a 3D functional prototype from a single RGBD image. Instead of precisely reconstructing the object geometry for the(More)
This paper presents a method to reconstruct a functional mechanical assembly from raw scans. Given multiple input scans of a mechanical assembly, our method first extracts the functional mechanical parts using a motion-guided, patch-based hierarchical registration and labeling algorithm. The extracted functional parts are then parameterized from the(More)