182 citations · 472 across the 28 of their papers we have counts for
29 papers
Why DDIM Hallucinates More Than DDPM: A Theoretical Analysis of Reverse Dynamics
Muhammad H. Ashiq, Samanyu Arora, Abhinav N. Harish +3
We theoretically study the hallucination phenomena in two canonical diffusion samplers: the stochastic Denoising Diffusion Probabilistic Model (DDPM) and the deterministic Denoisin…
DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos
Chieh Hubert Lin, Zhaoyang Lv, Songyin Wu +11
We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM), the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed vide…
Taming Latent Diffusion Model for Neural Radiance Field Inpainting
Chieh Hubert Lin, Changil Kim, Jia-Bin Huang +5
Neural Radiance Field (NeRF) is a representation for 3D reconstruction from multi-view images. Despite some recent work showing preliminary success in editing a reconstructed NeRF…
Exploiting Diffusion Prior for Generalizable Dense Prediction
Hsin-Ying Lee, Hung-Yu Tseng, Ming-Hsuan Yang
Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitiga…
Motion-Conditioned Diffusion Model for Controllable Video Synthesis
Tsai-Shien Chen, Chieh Hubert Lin, Hung-Yu Tseng +2
Recent advancements in diffusion models have greatly improved the quality and diversity of synthesized content. To harness the expressive power of diffusion models, researchers hav…
Consistent View Synthesis with Pose-Guided Diffusion Models
Hung-Yu Tseng, Qinbo Li, Changil Kim +3
Novel view synthesis from a single image has been a cornerstone problem for many Virtual Reality applications that provide immersive experiences. However, most existing techniques…
Robust Dynamic Radiance Fields
Yu-Lun Liu, Chen Gao, Andreas Meuleman +6
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera pose…
Adaptively-Realistic Image Generation from Stroke and Sketch with Diffusion Model
Shin-I Cheng, Yu-Jie Chen, Wei-Chen Chiu +2
Generating images from hand-drawings is a crucial and fundamental task in content creation. The translation is difficult as there exist infinite possibilities and the different use…
Vector Quantized Image-to-Image Translation
Yu-Jie Chen, Shin-I Cheng, Wei-Chen Chiu +2
Current image-to-image translation methods formulate the task with conditional generation models, leading to learning only the recolorization or regional changes as being constrain…
Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features
Chieh Hubert Lin, Hsin-Ying Lee, Hung-Yu Tseng +2
Recent studies show that paddings in convolutional neural networks encode absolute position information which can negatively affect the model performance for certain tasks. However…
Incremental False Negative Detection for Contrastive Learning
Tsai-Shien Chen, Wei-Chih Hung, Hung-Yu Tseng +2
Self-supervised learning has recently shown great potential in vision tasks through contrastive learning, which aims to discriminate each image, or instance, in the dataset. Howeve…
Learning to Stylize Novel Views
Hsin-Ping Huang, Hung-Yu Tseng, Saurabh Saini +2
We tackle a 3D scene stylization problem - generating stylized images of a scene from arbitrary novel views given a set of images of the same scene and a reference image of the des…
Stylizing 3D Scene via Implicit Representation and HyperNetwork
Pei-Ze Chiang, Meng-Shiun Tsai, Hung-Yu Tseng +2
In this work, we aim to address the 3D scene stylization problem - generating stylized images of the scene at arbitrary novel view angles. A straightforward solution is to combine…
Regularizing Generative Adversarial Networks under Limited Data
Hung-Yu Tseng, Lu Jiang, Ce Liu +2
Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This wor…
Unsupervised Sound Localization via Iterative Contrastive Learning
Yan-Bo Lin, Hung-Yu Tseng, Hsin-Ying Lee +2
Sound localization aims to find the source of the audio signal in the visual scene. However, it is labor-intensive to annotate the correlations between the signals sampled from the…
Unsupervised Discovery of Disentangled Manifolds in GANs
Yu-Ding Lu, Hsin-Ying Lee, Hung-Yu Tseng +1
As recent generative models can generate photo-realistic images, people seek to understand the mechanism behind the generation process. Interpretable generation process is benefici…
Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors
Qi Mao, Hung-Yu Tseng, Hsin-Ying Lee +3
Recent image-to-image (I2I) translation algorithms focus on learning the mapping from a source to a target domain. However, the continuous translation problem that synthesizes inte…
Semantic View Synthesis
Hsin-Ping Huang, Hung-Yu Tseng, Hsin-Ying Lee +1
We tackle a new problem of semantic view synthesis -- generating free-viewpoint rendering of a synthesized scene using a semantic label map as input. We build upon recent advances…
Text as Neural Operator: Image Manipulation by Text Instruction
Tianhao Zhang, Hung-Yu Tseng, Lu Jiang +3
In recent years, text-guided image manipulation has gained increasing attention in the multimedia and computer vision community. The input to conditional image generation has evolv…
RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval
Hung-Yu Tseng, Hsin-Ying Lee, Lu Jiang +2
Image generation from scene description is a cornerstone technique for the controlled generation, which is beneficial to applications such as content creation and image editing. In…
Modeling Artistic Workflows for Image Generation and Editing
Hung-Yu Tseng, Matthew Fisher, Jingwan Lu +3
People often create art by following an artistic workflow involving multiple stages that inform the overall design. If an artist wishes to modify an earlier decision, significant w…
Regularizing Meta-Learning via Gradient Dropout
Hung-Yu Tseng, Yi-Wen Chen, Yi-Hsuan Tsai +3
With the growing attention on learning-to-learn new tasks using only a few examples, meta-learning has been widely used in numerous problems such as few-shot classification, reinfo…
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang +1
Few-shot classification aims to recognize novel categories with only few labeled images in each class. Existing metric-based few-shot classification algorithms predict categories b…
Progressive Domain Adaptation for Object Detection
Han-Kai Hsu, Chun-Han Yao, Yi-Hsuan Tsai +4
Recent deep learning methods for object detection rely on a large amount of bounding box annotations. Collecting these annotations is laborious and costly, yet supervised models do…