UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation

• URL: http://arxiv.org/abs/2604.10485v1
• Date: Sun, 12 Apr 2026 06:37:01 GMT
• Title: UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation
• Abstract summary: We propose Unsupervised Domain Adaptation for Pose Estimation (UDAPose)<n>UDAPose synthesizes low-light images and dynamically fuses visual cues with pose priors for improved pose estimation.<n> Experiments show that UDAPose outperforms state-of-the-art methods.
• Score: 29.049710327499607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-visibility scenarios, such as low-light conditions, pose significant challenges to human pose estimation due to the scarcity of annotated low-light datasets and the loss of visual information under poor illumination. Recent domain adaptation techniques attempt to utilize well-lit labels by augmenting well-lit images to mimic low-light conditions. But handcrafted augmentations oversimplify noise patterns, while learning-based methods often fail to preserve high-frequency low-light characteristics, producing unrealistic images that lead pose models to generalize poorly to real low-light scenes. Moreover, recent pose estimators rely on image cues through image-to-keypoint cross-attention, but these cues become unreliable under low-light conditions. To address these issues, we propose Unsupervised Domain Adaptation for Pose Estimation (UDAPose), a novel framework that synthesizes low-light images and dynamically fuses visual cues with pose priors for improved pose estimation. Specifically, our synthesis method incorporates a Direct-Current-based High-Pass Filter (DHF) and a Low-light Characteristics Injection Module (LCIM) to inject high-frequency details from input low-light images, overcoming rigidity or the detail loss in existing approaches. Furthermore, we introduce a Dynamic Control of Attention (DCA) module that adaptively balances image cues with learned pose priors in the Transformer architecture. Experiments show that UDAPose outperforms state-of-the-art methods, with notable AP gains of 10.1 (56.4%) on the ExLPose-test hard set (LL-H) and 7.4 (31.4%) in cross-dataset validation on EHPT-XC. Code: https://github.com/Vision-and-Multimodal-Intelligence-Lab/UDAPose

Related papers

• Robust Low-Light Human Pose Estimation through Illumination-Texture Modulation [45.37915061527004]
  Current methods fail to provide high-quality representations due to reliance on pixel-level enhancements.<n>We propose a frequency-based framework for low-light human pose estimation, rooted in the "divide-and-conquer" principle.
  arXiv  Detail & Related papers  (2025-01-14T11:42:54Z)
• Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors [38.96909959677438]
  Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep neural networks, which require lots of low-normal light image pairs, network parameters, and computational resources. We devise a novel unsupervised LIE framework based on diffusion priors and lookup tables to achieve efficient low-light image recovery.
  arXiv  Detail & Related papers  (2024-09-27T16:37:27Z)
• Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement [71.13353154514418]
  Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge.<n>We propose a novel Mamba-based method customized for low light RAW images, called RAWMamba, to effectively handle raw images with different CFAs.<n>By bridging demosaicing and denoising, better enhancement for low light RAW images is achieved.
  arXiv  Detail & Related papers  (2024-09-11T06:12:03Z)
• Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation [33.142262765252795]
  Detectors trained on well-lit data exhibit significant performance degradation on low-light data due to low visibility. We propose to boost low-light object detection with zero-shot day-night domain adaptation. Our method generalizes a detector from well-lit scenarios to low-light ones without requiring real low-light data.
  arXiv  Detail & Related papers  (2023-12-02T20:11:48Z)
• LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models [54.93010869546011]
  We propose to leverage the pre-trained latent diffusion model to perform the neural ISP for enhancing extremely low-light images.<n>Specifically, to tailor the pre-trained latent diffusion model to operate on the RAW domain, we train a set of lightweight taming modules.<n>We observe different roles of UNet denoising and decoder reconstruction in the latent diffusion model, which inspires us to decompose the low-light image enhancement task into latent-space low-frequency content generation and decoding-phase high-frequency detail maintenance.
  arXiv  Detail & Related papers  (2023-12-02T04:31:51Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• Human Pose Estimation in Extremely Low-Light Conditions [21.210706205233286]
  We develop a dedicated camera system and build a new dataset of real low-light images with accurate pose labels. Thanks to our camera system, each low-light image in our dataset is coupled with an aligned well-lit image, which enables accurate pose labeling. We also propose a new model and a new training strategy that fully exploit the privileged information to learn representation insensitive to lighting conditions.
  arXiv  Detail & Related papers  (2023-03-27T17:28:25Z)
• Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition [78.50328335703914]
  Diffusion in the Dark (DiD) is a diffusion model for low-light image reconstruction for text recognition. We demonstrate that DiD, without any task-specific optimization, can outperform SOTA low-light methods in low-light text recognition on real images.
  arXiv  Detail & Related papers  (2023-03-07T23:52:51Z)
• Deep Bilateral Retinex for Low-Light Image Enhancement [96.15991198417552]
  Low-light images suffer from poor visibility caused by low contrast, color distortion and measurement noise. This paper proposes a deep learning method for low-light image enhancement with a particular focus on handling the measurement noise. The proposed method is very competitive to the state-of-the-art methods, and has significant advantage over others when processing images captured in extremely low lighting conditions.
  arXiv  Detail & Related papers  (2020-07-04T06:26:44Z)
• Unsupervised Low-light Image Enhancement with Decoupled Networks [103.74355338972123]
  We learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. Our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.
  arXiv  Detail & Related papers  (2020-05-06T13:37:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.