Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟴𝟰 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝗣𝗔𝗠𝘀: 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗜𝗺𝗽𝗹𝗶𝗰𝗶𝘁 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗿𝗶𝗰 𝗠𝗼𝗱𝗲𝗹𝘀 𝗯𝘆 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗼𝗳 𝗠𝘂𝗻𝗶𝗰𝗵 𝗮𝗻𝗱 𝗠𝗲𝘁𝗮 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗟𝗮𝗯𝘀 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵, 𝗦𝗮𝘂𝘀𝗮𝗹𝗶𝘁𝗼, 𝗨𝗦𝗔 Follow me for a similar post: Ashish Patel  ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗦𝗣𝗔𝗠𝘀: 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗜𝗺𝗽𝗹𝗶𝗰𝗶𝘁 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗿𝗶𝗰 𝗠𝗼𝗱𝗲𝗹𝘀 🔸 This paper is published arxiv2022. 𝗤𝘂𝗲 : 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗿𝗶𝗰 𝗼𝗯𝗷𝗲𝗰𝘁 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴? Ans : Parametric modelling is a modeling process with the ability to change the shape of model geometry as soon as the dimension value is modified. ... The model can be visualized in 3D draughting programs to resemble the attributes of the real behavior of the original project. 𝗤𝘂𝗲 : 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗿𝗶𝗰 𝗮𝗻𝗱 𝗱𝗶𝗿𝗲𝗰𝘁 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴? Ans : Direct modeling is an effective, quick, and straightforward way to explore ideas and design variations, especially in the creative phase of a design project. On the other hand, parametric modeling is a systematic, mathematical approach to 3D design. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Parametric 3D models have formed a fundamental role in modeling deformable objects, such as human bodies, faces, and hands; however, the construction of such parametric models requires significant manual intervention and domain expertise.  🔸 Recently, neural implicit 3D representations have shown great expressibility in capturing 3D shape geometry.  🔸 We observe that deformable object motion is often semantically structured, and thus propose to learn Structured-implicit PArametric Models (SPAMs) as a deformable object representation that structurally decomposes non-rigid object motion into part-based disentangled representations of shape and pose, with each being represented by deep implicit functions.  🔸 This enables a structured characterization of object movement, with part decomposition characterizing a lower-dimensional space in which we can establish coarse motion correspondence.  🔸 In particular, we can leverage the part decompositions at test time to fit to new depth sequences of unobserved shapes, by establishing part correspondences between the input observation and our learned part spaces; this guides a robust joint optimization between the shape and pose of all parts, even under dramatic motion sequences.  🔸 Experiments demonstrate that our part-aware shape and pose understanding lead to state-of-the-art performance in reconstruction and tracking of depth sequences of complex deforming object motion.  #computervision #artificialintelligence #innovation