Datasets:

Seetha
/

visual_files

- [{"": "3", "Id": "0", "Fullsentence": "the optimization is thus bootstrapped and leads tosubstantial texture boosting", "Component": "tosubstantial texture boosting", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "5", "Id": "1", "Fullsentence": "to deal with the oversaturation and blurriness issues recent works adopt stagewise optimization strategies mildenhallet al 2021 or propose improved 2d distillation loss wang et al 2023b which leads to an enhancement in photorealism", "Component": "blur ss", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "7", "Id": "1", "Fullsentence": "to deal with the oversaturation and blurriness issues recent works adopt stagewise optimization strategies mildenhallet al 2021 or propose improved 2d distillation loss wang et al 2023b which leads to an enhancement in photorealism", "Component": "improved 2d distillation loss wang et", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "10", "Id": "1", "Fullsentence": "to deal with the oversaturation and blurriness issues recent works adopt stagewise optimization strategies mildenhallet al 2021 or propose improved 2d distillation loss wang et al 2023b which leads to an enhancement in photorealism", "Component": "an enhancement in photorealism", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "11", "Id": "2", "Fullsentence": "contrary toprior approaches our work highlights how careful consideration of each stage can unleash the fullpotential of hierarchical generation resulting in superiorquality 3d creationthe geometry sculpting stage aims to produce plausible and consistent 3d geometry from the 2dreference image", "Component": "careful consideration of each stage can unleash the fullpotential of hierarchical generation resulting in superiorquality 3d creation", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "13", "Id": "2", "Fullsentence": "contrary toprior approaches our work highlights how careful consideration of each stage can unleash the fullpotential of hierarchical generation resulting in superiorquality 3d creationthe geometry sculpting stage aims to produce plausible and consistent 3d geometry from the 2dreference image", "Component": "s cu", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "17", "Id": "2", "Fullsentence": "contrary toprior approaches our work highlights how careful consideration of each stage can unleash the fullpotential of hierarchical generation resulting in superiorquality 3d creationthe geometry sculpting stage aims to produce plausible and consistent 3d geometry from the 2dreference image", "Component": "produce plausible and consistent 3d geometry from the 2dreference image", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "18", "Id": "3", "Fullsentence": "additionally we find annealing the sampling timestep and progressively enlarging training views are crucial to further improve coherency", "Component": "annealing the sampling timestep", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "19", "Id": "3", "Fullsentence": "additionally we find annealing the sampling timestep and progressively enlarging training views are crucial to further improve coherency", "Component": "progressively enlarging training views", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "22", "Id": "3", "Fullsentence": "additionally we find annealing the sampling timestep and progressively enlarging training views are crucial to further improve coherency", "Component": "improve coherency", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "23", "Id": "4", "Fullsentence": "importantly we find that alternativelyoptimizing the generative prior and 3d representation leads to mutually reinforcing improvementsthe diffusion model benefits from training on improved multiview renderings which in turn provides superior guidance for optimizing the 3d texture", "Component": "alternativelyoptimizing the generative prior and 3d representation", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "25", "Id": "4", "Fullsentence": "importantly we find that alternativelyoptimizing the generative prior and 3d representation leads to mutually reinforcing improvementsthe diffusion model benefits from training on improved multiview renderings which in turn provides superior guidance for optimizing the 3d texture", "Component": "mutually reinforcing improvementsthe diffusion model benefits from training on improved multiview renderings", "causeOrEffect": "effect", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "26", "Id": "4", "Fullsentence": "importantly we find that alternativelyoptimizing the generative prior and 3d representation leads to mutually reinforcing improvementsthe diffusion model benefits from training on improved multiview renderings which in turn provides superior guidance for optimizing the 3d texture", "Component": "provides superior guidance for optimizing the 3d texture", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "27", "Id": "5", "Fullsentence": "however these methods require 3d shapes or multiview data for training raisingchallenges when generating inthewild 3d assets due to the scarcity of diverse 3d data changet al 2015 deitke et al 2023 wu et al 2023 compared to 2d3daware image generation aims to render images in novel views while offering some level of3d consistency", "Component": "these methods require 3d shapes or multiview data for training raisingchallenges when generating inthewild 3d assets", "causeOrEffect": "effect", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "29", "Id": "5", "Fullsentence": "however these methods require 3d shapes or multiview data for training raisingchallenges when generating inthewild 3d assets due to the scarcity of diverse 3d data changet al 2015 deitke et al 2023 wu et al 2023 compared to 2d3daware image generation aims to render images in novel views while offering some level of3d consistency", "Component": "scarcity of diverse 3d data changet al 2015 deitke et al 2023 wu et al 2023 compared to 2d3daware image generation aims to render images in novel views", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "30", "Id": "5", "Fullsentence": "however these methods require 3d shapes or multiview data for training raisingchallenges when generating inthewild 3d assets due to the scarcity of diverse 3d data changet al 2015 deitke et al 2023 wu et al 2023 compared to 2d3daware image generation aims to render images in novel views while offering some level of3d consistency", "Component": "offering some level of3d consistency", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "32", "Id": "6", "Fullsentence": "thus we employ additional techniques to produce coherent detailed geometry3daware diffusion prior", "Component": "employ additional techniques to", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "33", "Id": "6", "Fullsentence": "thus we employ additional techniques to produce coherent detailed geometry3daware diffusion prior", "Component": "produce coherent detailed geometry3 da", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "36", "Id": "7", "Fullsentence": "however the finetuning onlimited categories of 3d data of inferior rendering quality impairs the diffusion models generationcapability so the 3daware sds loss alone is prone to induce deteriorated quality when liftinggeneral images to 3d", "Component": "finetuning onlimited categories of 3d data of inferior rendering quality impairs the diffusion models generationcapability", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "38", "Id": "7", "Fullsentence": "however the finetuning onlimited categories of 3d data of inferior rendering quality impairs the diffusion models generationcapability so the 3daware sds loss alone is prone to induce deteriorated quality when liftinggeneral images to 3d", "Component": "3daware sds loss alone is prone to induce deteriorated quality when liftinggeneral images to 3d", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "39", "Id": "8", "Fullsentence": "however directly deriving the free views in 360may still result ingeometric artifacts such as extra chair legs due to the ambiguity inherent in a single referenceimage", "Component": "free views ma", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "41", "Id": "8", "Fullsentence": "however directly deriving the free views in 360may still result ingeometric artifacts such as extra chair legs due to the ambiguity inherent in a single referenceimage", "Component": "result ingeometric artifacts such as extra chair legs", "causeOrEffect": "effect", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "43", "Id": "8", "Fullsentence": "however directly deriving the free views in 360may still result ingeometric artifacts such as extra chair legs due to the ambiguity inherent in a single referenceimage", "Component": "ambiguity inherent in a single referenceimage", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "45", "Id": "9", "Fullsentence": "this is due to our reliance on a 2d prior model that operates at a coarse resolutionand the limited sharpness offered by the 3daware diffusion model", "Component": "reliance on a 2d prior model that operates at a coarse resolutionand the limited sharpness offered by the 3daware diffusion model", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "46", "Id": "10", "Fullsentence": "as the 3d mesh reveals finer textures we reduce the diffusion noises introduced tothe image renderings so the dreambooth model learns from more consistent renderings and bettercaptures the image distribution faithful to evolving views", "Component": "3d mesh reveals finer textures we reduce the diffusion noises introduced tothe image rendering", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "48", "Id": "10", "Fullsentence": "as the 3d mesh reveals finer textures we reduce the diffusion noises introduced tothe image renderings so the dreambooth model learns from more consistent renderings and bettercaptures the image distribution faithful to evolving views", "Component": "the dreambooth model learns from more consistent renderings and bettercaptures the image distribution faithful to evolving views", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "49", "Id": "11", "Fullsentence": "the initial stageinvolves the introduction of substantial noise into each image to amplify detail richness leading toinconsistent denoised images", "Component": "initial stageinvolves the introduction of substantial noise into each image to amplify detail richness", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "51", "Id": "11", "Fullsentence": "the initial stageinvolves the introduction of substantial noise into each image to amplify detail richness leading toinconsistent denoised images", "Component": "toinconsistent denoised images", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "52", "Id": "12", "Fullsentence": "however as the textured mesh undergoes optimization the producedrenderings evolve towards increased consistency and photorealism thereby enhancing the quality ofthe input dataset tailored for dreambooth6 c onclusionwe have presented dreamcraft3d an innovative approach that advances the field of complex 3dasset generation", "Component": "producedrenderings evolve towards increased consistency and photorealism", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "54", "Id": "12", "Fullsentence": "however as the textured mesh undergoes optimization the producedrenderings evolve towards increased consistency and photorealism thereby enhancing the quality ofthe input dataset tailored for dreambooth6 c onclusionwe have presented dreamcraft3d an innovative approach that advances the field of complex 3dasset generation", "Component": "enhancing the quality ofthe input dataset tailored for dreambooth6 c onclusionwe have presented dreamcraft3d an innovative approach that advances the field of complex 3dasset generation", "causeOrEffect": "effect", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "55", "Id": "13", "Fullsentence": "2022 include an initial spatial density bias in order to encourage optimization in favor of the objectcentric neural fieldcamera and light augmentations", "Component": "2022 include an initial spatial density bias", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "57", "Id": "13", "Fullsentence": "2022 include an initial spatial density bias in order to encourage optimization in favor of the objectcentric neural fieldcamera and light augmentations", "Component": "encourage optimization in favor of the objectcentric neural fieldcamera and light augmentations", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "58", "Id": "14", "Fullsentence": "the originis sampled from \u03d5cam u0 \u03c03with a random point light distance rcam7510 and bwe freeze the material augmentation unlike dreamfusion and magic3d as we found it is bad fortraining convergence c in the coarse neus stage we propose a fixedrandom mixed camera posestrategy", "Component": "originis sampled from \u03d5cam u0 \u03c03", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "59", "Id": "14", "Fullsentence": "the originis sampled from \u03d5cam u0 \u03c03with a random point light distance rcam7510 and bwe freeze the material augmentation unlike dreamfusion and magic3d as we found it is bad fortraining convergence c in the coarse neus stage we propose a fixedrandom mixed camera posestrategy", "Component": "random point light distance rcam75", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "60", "Id": "14", "Fullsentence": "the originis sampled from \u03d5cam u0 \u03c03with a random point light distance rcam7510 and bwe freeze the material augmentation unlike dreamfusion and magic3d as we found it is bad fortraining convergence c in the coarse neus stage we propose a fixedrandom mixed camera posestrategy", "Component": "freeze the material augmentation unlike dreamfusion", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "62", "Id": "14", "Fullsentence": "the originis sampled from \u03d5cam u0 \u03c03with a random point light distance rcam7510 and bwe freeze the material augmentation unlike dreamfusion and magic3d as we found it is bad fortraining convergence c in the coarse neus stage we propose a fixedrandom mixed camera posestrategy", "Component": "bad fortraining convergence c in the coarse neus stage", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "63", "Id": "14", "Fullsentence": "the originis sampled from \u03d5cam u0 \u03c03with a random point light distance rcam7510 and bwe freeze the material augmentation unlike dreamfusion and magic3d as we found it is bad fortraining convergence c in the coarse neus stage we propose a fixedrandom mixed camera posestrategy", "Component": "propose a fixedrandom mixed camera posestrategy", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "64", "Id": "15", "Fullsentence": "figure 8 demonstrates the proficiency of our method in generating an array of diversemodels from a single text prompt all characterized by their remarkable qualitya3 l imitationsour approach occasionally incorporates frontalview geometric details into texture as depicted infigure 9 due to depth ambiguity and inaccuracies in the depth prior", "Component": "the proficiency of", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "67", "Id": "15", "Fullsentence": "figure 8 demonstrates the proficiency of our method in generating an array of diversemodels from a single text prompt all characterized by their remarkable qualitya3 l imitationsour approach occasionally incorporates frontalview geometric details into texture as depicted infigure 9 due to depth ambiguity and inaccuracies in the depth prior", "Component": "array of diversemodels", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "68", "Id": "15", "Fullsentence": "figure 8 demonstrates the proficiency of our method in generating an array of diversemodels from a single text prompt all characterized by their remarkable qualitya3 l imitationsour approach occasionally incorporates frontalview geometric details into texture as depicted infigure 9 due to depth ambiguity and inaccuracies in the depth prior", "Component": "occasionally incorporates frontalview geometric details into texture as depicted infigure 9", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "70", "Id": "15", "Fullsentence": "figure 8 demonstrates the proficiency of our method in generating an array of diversemodels from a single text prompt all characterized by their remarkable qualitya3 l imitationsour approach occasionally incorporates frontalview geometric details into texture as depicted infigure 9 due to depth ambiguity and inaccuracies in the depth prior", "Component": "depth ambiguity and inaccuracies in the depth prior", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}]