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51 to 60 of 69 Results

VToonify Oct 1, 2024 - Chen Change LOY Loy, Chen Change; Yang, Shuai, 2024, "VToonify", https://doi.org/10.21979/N9/7PGAOA, DR-NTU (Data), V4 Generating high-quality artistic portrait videos is an important and desirable task in computer graphics and vision. Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious...
FreeInit: Bridging Initialization Gap in Video Diffusion Models Sep 27, 2024 Wu, Tianxing; Si, Chenyang; Jiang, Yuming; Huang, Ziqi; Liu, Ziwei, 2024, "FreeInit: Bridging Initialization Gap in Video Diffusion Models", https://doi.org/10.21979/N9/JMCW1W, DR-NTU (Data), V1 Though diffusion-based video generation has witnessed rapid progress, the inference results of existing models still exhibit unsatisfactory temporal consistency and unnatural dynamics. In this paper, we delve deep into the noise initialization of video diffusion models, and disco...
LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation Sep 27, 2024 Lan, Yushi; Fangzhou Hong; Shuai Yang; Shangchen Zhou; Bo Dai; Xingang Pan; Chen Change Loy, 2024, "LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation", https://doi.org/10.21979/N9/UZ06ZG, DR-NTU (Data), V1 The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework ca...
ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance Sep 27, 2024 Chen, Yongwei; Wang, Tengfei; Wu, Tong; Pan, Xingang; Jia, Kui; Liu, Ziwei, 2024, "ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance", https://doi.org/10.21979/N9/BAZCX6, DR-NTU (Data), V1 Generating high-quality 3D assets from a given image is highly desirable in various applications such as AR/VR. Recent advances in single-image 3D generation explore feed-forward models that learn to infer the 3D model of an object without optimization. Though promising results h...
LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation Sep 26, 2024 Tang, Jiaxiang; Chen, Zhaoxi; Chen, Xiaokang; Wang, Tengfei; Zeng, Gang; Liu, Ziwei, 2024, "LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation", https://doi.org/10.21979/N9/27JLJB, DR-NTU (Data), V1 3D content creation has achieved significant progress in terms of both quality and speed. Although current feed-forward models can produce 3D objects in seconds, their resolution is constrained by the intensive computation required during training. In this paper, we introduce Lar...
ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation Sep 25, 2024 Lan, Mengcheng; Chen, Chaofeng; Ke, Yiping; Wang, Xinjiang; Feng, Litong; Zhang, Wayne, 2024, "ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation", https://doi.org/10.21979/N9/YY8L5O, DR-NTU (Data), V1 Open-vocabulary semantic segmentation requires models to effectively integrate visual representations with open-vocabulary semantic labels. While Contrastive Language-Image Pre-training (CLIP) models shine in recognizing visual concepts from text, they often struggle with segment...
ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference Sep 25, 2024 Lan, Mengcheng; Chen, Chaofeng; Ke, Yiping; Wang, Xinjiang; Feng, Litong; Zhang, Wayne, 2024, "ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference", https://doi.org/10.21979/N9/S6NTDJ, DR-NTU (Data), V1 Despite the success of large-scale pretrained Vision-Language Models (VLMs) especially CLIP in various open-vocabulary tasks, their application to semantic segmentation remains challenging, producing noisy segmentation maps with mis-segmented regions. In this paper, we carefully...
Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively Sep 25, 2024 Yuan, Haobo; Li, Xiangtai; Zhou, Chong; Li, Yining; Chen, Kai; Loy, Chen Change, 2024, "Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively", https://doi.org/10.21979/N9/L05ULT, DR-NTU (Data), V1 The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is renowned for its zero-shot recognition capabilities. This paper presents an in-depth exploration of integrating these...
ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition Sep 25, 2024 Wu, Tianhao; Zheng, Chuanxia; Wu, Qianyi; Cham, Tat-Jen, 2024, "ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition", https://doi.org/10.21979/N9/RJUHMC, DR-NTU (Data), V1 3D decomposition/segmentation remains a challenge as large-scale 3D annotated data is not readily available. Existing approaches typically leverage 2D machine-generated segments, integrating them to achieve 3D consistency. In this paper, we propose ClusteringSDF, a novel approach...
Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering Sep 25, 2024 Xu, Baixin; Hu, Jiangbei; Hou, Fei; Lin, Kwan-Yee; Wu, Wayne; Qian, Chen; He, Ying, 2024, "Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering", https://doi.org/10.21979/N9/0C9BU9, DR-NTU (Data), V1 The advancements in neural rendering have increased the need for techniques that enable intuitive editing of 3D objects represented as neural implicit surfaces. This paper introduces a novel neural algorithm for parameterizing neural implicit surfaces to simple parametric domains...

VToonify

Oct 1, 2024 - Chen Change LOY

Loy, Chen Change; Yang, Shuai, 2024, "VToonify", https://doi.org/10.21979/N9/7PGAOA, DR-NTU (Data), V4

Generating high-quality artistic portrait videos is an important and desirable task in computer graphics and vision. Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious...

FreeInit: Bridging Initialization Gap in Video Diffusion Models

Sep 27, 2024

Wu, Tianxing; Si, Chenyang; Jiang, Yuming; Huang, Ziqi; Liu, Ziwei, 2024, "FreeInit: Bridging Initialization Gap in Video Diffusion Models", https://doi.org/10.21979/N9/JMCW1W, DR-NTU (Data), V1

Though diffusion-based video generation has witnessed rapid progress, the inference results of existing models still exhibit unsatisfactory temporal consistency and unnatural dynamics. In this paper, we delve deep into the noise initialization of video diffusion models, and disco...

LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation

Sep 27, 2024

Lan, Yushi; Fangzhou Hong; Shuai Yang; Shangchen Zhou; Bo Dai; Xingang Pan; Chen Change Loy, 2024, "LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation", https://doi.org/10.21979/N9/UZ06ZG, DR-NTU (Data), V1

The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework ca...

ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance

Sep 27, 2024

Chen, Yongwei; Wang, Tengfei; Wu, Tong; Pan, Xingang; Jia, Kui; Liu, Ziwei, 2024, "ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance", https://doi.org/10.21979/N9/BAZCX6, DR-NTU (Data), V1

Generating high-quality 3D assets from a given image is highly desirable in various applications such as AR/VR. Recent advances in single-image 3D generation explore feed-forward models that learn to infer the 3D model of an object without optimization. Though promising results h...

LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation

Sep 26, 2024

Tang, Jiaxiang; Chen, Zhaoxi; Chen, Xiaokang; Wang, Tengfei; Zeng, Gang; Liu, Ziwei, 2024, "LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation", https://doi.org/10.21979/N9/27JLJB, DR-NTU (Data), V1

3D content creation has achieved significant progress in terms of both quality and speed. Although current feed-forward models can produce 3D objects in seconds, their resolution is constrained by the intensive computation required during training. In this paper, we introduce Lar...

ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation

Sep 25, 2024

Lan, Mengcheng; Chen, Chaofeng; Ke, Yiping; Wang, Xinjiang; Feng, Litong; Zhang, Wayne, 2024, "ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation", https://doi.org/10.21979/N9/YY8L5O, DR-NTU (Data), V1

Open-vocabulary semantic segmentation requires models to effectively integrate visual representations with open-vocabulary semantic labels. While Contrastive Language-Image Pre-training (CLIP) models shine in recognizing visual concepts from text, they often struggle with segment...

ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference

Sep 25, 2024

Lan, Mengcheng; Chen, Chaofeng; Ke, Yiping; Wang, Xinjiang; Feng, Litong; Zhang, Wayne, 2024, "ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference", https://doi.org/10.21979/N9/S6NTDJ, DR-NTU (Data), V1

Despite the success of large-scale pretrained Vision-Language Models (VLMs) especially CLIP in various open-vocabulary tasks, their application to semantic segmentation remains challenging, producing noisy segmentation maps with mis-segmented regions. In this paper, we carefully...

Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Sep 25, 2024

Yuan, Haobo; Li, Xiangtai; Zhou, Chong; Li, Yining; Chen, Kai; Loy, Chen Change, 2024, "Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively", https://doi.org/10.21979/N9/L05ULT, DR-NTU (Data), V1

The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is renowned for its zero-shot recognition capabilities. This paper presents an in-depth exploration of integrating these...

ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition

Sep 25, 2024

Wu, Tianhao; Zheng, Chuanxia; Wu, Qianyi; Cham, Tat-Jen, 2024, "ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition", https://doi.org/10.21979/N9/RJUHMC, DR-NTU (Data), V1

3D decomposition/segmentation remains a challenge as large-scale 3D annotated data is not readily available. Existing approaches typically leverage 2D machine-generated segments, integrating them to achieve 3D consistency. In this paper, we propose ClusteringSDF, a novel approach...

Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering

Sep 25, 2024

Xu, Baixin; Hu, Jiangbei; Hou, Fei; Lin, Kwan-Yee; Wu, Wayne; Qian, Chen; He, Ying, 2024, "Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering", https://doi.org/10.21979/N9/0C9BU9, DR-NTU (Data), V1

The advancements in neural rendering have increased the need for techniques that enable intuitive editing of 3D objects represented as neural implicit surfaces. This paper introduces a novel neural algorithm for parameterizing neural implicit surfaces to simple parametric domains...

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