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2305.03027
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# NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads TOBIAS KIRSCHSTEIN, Technical University of Munich, Germany SHENHAN QIAN, Technical University of Munich, Germany SIMON GIEBENHAIN, Technical University of Munich, Germany TIM WALTER, Technical University of Munich, Germany MATTHIAS NIESSNER, Technic...
<h1>NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads</h1> <p>TOBIAS KIRSCHSTEIN, Technical University of Munich, Germany SHENHAN QIAN, Technical University of Munich, Germany SIMON GIEBENHAIN, Technical University of Munich, Germany TIM WALTER, Technical University of Munich, Germany MATTHIAS NIESSNER...
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2305.03027
1
is typically very low. In particular, human heads exhibit several properties that make novel view synthesis (NVS) extremely chal- lenging, such as the complexity of hair, differences in reflectance properties, and the elasticity of human skin that creates heavily non-rigid deformations and fine-scale wrinkles. In the ...
<p>is typically very low. In particular, human heads exhibit several properties that make novel view synthesis (NVS) extremely chal- lenging, such as the complexity of hair, differences in reflectance properties, and the elasticity of human skin that creates heavily non-rigid deformations and fine-scale wrinkles.</p> <...
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[{"type": "text", "text": "", "page_idx": 1}, {"type": "text", "text": "In the context of static scenes, we have seen neural radiance field representations (NeRFs) [Mildenhall et al. 2020] obtain compelling NVS results. The core idea of this seminal work is to leverage a volumetric rendering formulation as a reconstruc...
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2305.03027
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"Contribution of Architectural Components. We ablate the effect of\nusing a hash ensemble and the de(...TRUNCATED)
"<p>Contribution of Architectural Components. We ablate the effect of\nusing a hash ensemble and the(...TRUNCATED)
"[{\"type\": \"image\", \"coordinates\": [52, 77, 558, 302], \"content\": \"\", \"block_type\": \"im(...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [61, 375, 295, 384], \"content\": \"Contribution of Architec(...TRUNCATED)
"[{\"coordinates\": [52, 77, 558, 302], \"index\": 3.0, \"caption\": \"sharp detail already returns (...TRUNCATED)
"[{\"type\": \"inline\", \"coordinates\": [268, 396, 295, 405], \"content\": \"(\\\\mathrm{NGP~+~}\"(...TRUNCATED)
[]
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"[{\"type\": \"image\", \"img_path\": \"images/4c08c14de2f416c56b99f18635ac6f1bae045b127331a2e699473(...TRUNCATED)
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2305.03027
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"Content of Individual Hash Grids. We analyze the contents of the\nindividual hash grids $$\\mathcal(...TRUNCATED)
"<p>Content of Individual Hash Grids. We analyze the contents of the\nindividual hash grids $$\\math(...TRUNCATED)
"[{\"type\": \"image\", \"coordinates\": [51, 77, 296, 172], \"content\": \"\", \"block_type\": \"im(...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [60, 259, 294, 270], \"content\": \"Content of Individual Ha(...TRUNCATED)
"[{\"coordinates\": [51, 77, 296, 172], \"index\": 3.25, \"caption\": \" denotes the first frame.\",(...TRUNCATED)
"[{\"type\": \"inline\", \"coordinates\": [132, 270, 144, 280], \"content\": \"\\\\mathcal{H}_{i}\",(...TRUNCATED)
[]
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"[{\"type\": \"image\", \"img_path\": \"images/c3276da60dd007d19d9f774856b5a11a0b6bb9b63e68638e85a72(...TRUNCATED)
"[{\"category_id\": 1, \"poly\": [138.63345336914062, 1562.0018310546875, 822.1433715820312, 1562.00(...TRUNCATED)
"{\"preproc_blocks\": [{\"type\": \"image\", \"bbox\": [51, 77, 296, 172], \"blocks\": [{\"type\": \(...TRUNCATED)
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"Michael Broxton, John Flynn, Ryan Overbeck, Daniel Erickson, Peter Hedman, Matthew\nDuvall, Jason D(...TRUNCATED)
"<p>Michael Broxton, John Flynn, Ryan Overbeck, Daniel Erickson, Peter Hedman, Matthew\nDuvall, Jaso(...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [50, 79, 297, 689], \"content\": \"Michael Broxton, John Fly(...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [50, 82, 296, 92], \"content\": \"Michael Broxton, John Flyn(...TRUNCATED)
[]
"[{\"type\": \"inline\", \"coordinates\": [438, 536, 450, 545], \"content\": \"\\\\mathrm{Ng}^{\\\\a(...TRUNCATED)
[]
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"[{\"type\": \"text\", \"text\": \"\", \"page_idx\": 12}, {\"type\": \"text\", \"text\": \"\", \"pag(...TRUNCATED)
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"Cheng-hsin Wuu, Ningyuan Zheng, Scott Ardisson, Rohan Bali, Danielle Belko, Eric\nBrockmeyer, Lucas(...TRUNCATED)
"<p>Cheng-hsin Wuu, Ningyuan Zheng, Scott Ardisson, Rohan Bali, Danielle Belko, Eric\nBrockmeyer, Lu(...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [49, 82, 295, 227], \"content\": \"Cheng-hsin Wuu, Ningyuan (...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [51, 82, 294, 91], \"content\": \"Cheng-hsin Wuu, Ningyuan Z(...TRUNCATED)
[]
[]
[]
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"[{\"type\": \"text\", \"text\": \"\", \"page_idx\": 13}, {\"type\": \"text\", \"text\": \"\", \"pag(...TRUNCATED)
"[{\"category_id\": 1, \"poly\": [877.4862060546875, 228.1995391845703, 1563.5543212890625, 228.1995(...TRUNCATED)
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2305.03027
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"other hand, Li et al. [2022b] refrain from using explicit deforma-\ntions and instead encode the st(...TRUNCATED)
"<p>other hand, Li et al. [2022b] refrain from using explicit deforma-\ntions and instead encode the(...TRUNCATED)
"[{\"type\": \"image\", \"coordinates\": [50, 80, 560, 274], \"content\": \"\", \"block_type\": \"im(...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [51, 316, 295, 326], \"content\": \"other hand, Li et al. [2(...TRUNCATED)
"[{\"coordinates\": [50, 80, 560, 274], \"index\": 2.0, \"caption\": \"Fig. 2. Left: Our custom-buil(...TRUNCATED)
"[{\"type\": \"inline\", \"coordinates\": [345, 538, 357, 547], \"content\": \"93^{\\\\circ}\", \"ca(...TRUNCATED)
[]
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"[{\"type\": \"image\", \"img_path\": \"images/acfed8671b11540da9027122686f9fd7112341be8f37f18143bfb(...TRUNCATED)
"[{\"category_id\": 1, \"poly\": [143.4579315185547, 875.9676513671875, 820.0895385742188, 875.96765(...TRUNCATED)
"{\"preproc_blocks\": [{\"type\": \"image\", \"bbox\": [50, 80, 560, 274], \"blocks\": [{\"type\": \(...TRUNCATED)
2305.03027
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"order to maximize the variety of motion. Specifically, our capture\nscript consists of 9 expression(...TRUNCATED)
"<p>order to maximize the variety of motion. Specifically, our capture\nscript consists of 9 express(...TRUNCATED)
"[{\"type\": \"table\", \"coordinates\": [51, 101, 296, 129], \"content\": \"\", \"block_type\": \"t(...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [51, 343, 294, 353], \"content\": \"order to maximize the va(...TRUNCATED)
"[{\"coordinates\": [55, 126, 292, 294], \"index\": 14.75, \"caption\": \"quences feature a wide ran(...TRUNCATED)
[]
[]
[612.0, 792.0]
"[{\"type\": \"table\", \"img_path\": \"images/f9707d6954a293da079c8968b4d30337cc51e52e12dab78254bdc(...TRUNCATED)
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"# 4.1 Preliminaries: Neural Radiance Fields\n\nOur work builds on top of the recent success of Neur(...TRUNCATED)
"<h1>4.1 Preliminaries: Neural Radiance Fields</h1>\n<p>Our work builds on top of the recent success(...TRUNCATED)
"[{\"type\": \"image\", \"coordinates\": [48, 77, 559, 260], \"content\": \"\", \"block_type\": \"im(...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [51, 338, 226, 348], \"content\": \"4.1 Preliminaries: Neura(...TRUNCATED)
"[{\"coordinates\": [48, 77, 559, 260], \"index\": 3.0, \"caption\": \" from the blended features us(...TRUNCATED)
"[{\"type\": \"block\", \"coordinates\": [101, 402, 244, 438], \"content\": \"\", \"caption\": \"\"}(...TRUNCATED)
[]
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"[{\"type\": \"image\", \"img_path\": \"images/c7540b1718164c4caaba8f5f90071593dad073290846a6de23090(...TRUNCATED)
"[{\"category_id\": 8, \"poly\": [280.868408203125, 1119.9073486328125, 678.4691162109375, 1119.9073(...TRUNCATED)
"{\"preproc_blocks\": [{\"type\": \"image\", \"bbox\": [48, 77, 559, 260], \"blocks\": [{\"type\": \(...TRUNCATED)
2305.03027
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"Using these learned correspondences, we modify Equation 4 to\noperate in the canonical space:\n\nTh(...TRUNCATED)
"<p>Using these learned correspondences, we modify Equation 4 to\noperate in the canonical space:</p(...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [51, 80, 294, 102], \"content\": \"Using these learned corre(...TRUNCATED)
"[{\"type\": \"text\", \"coordinates\": [60, 80, 294, 92], \"content\": \"Using these learned corres(...TRUNCATED)
[]
"[{\"type\": \"block\", \"coordinates\": [117, 107, 227, 138], \"content\": \"\", \"caption\": \"\"}(...TRUNCATED)
[]
[612.0, 792.0]
"[{\"type\": \"text\", \"text\": \"Using these learned correspondences, we modify Equation 4 to oper(...TRUNCATED)
"[{\"category_id\": 9, \"poly\": [1517.369140625, 716.4633178710938, 1556.6090087890625, 716.4633178(...TRUNCATED)
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