CURL: Multi-view 3D Hair Reconstruction via Adaptive Gaussian Ellipsoids under Varying Illumination

While these representations provide a flexible framework for general scene reconstruction, they are insufficient for hair-specific challenges. In regions affected by poor lighting, sparse geometric signals lead to holes in reconstructed hair volumes, and conventional density control mechanisms lack the adaptability required to handle varying hairstyle complexity. Furthermore, the strong coupling between geometry and texture under complex illumination is often overlooked, limiting photorealistic hair reconstruction.

To overcome these limitations, we propose a novel multi-view hair reconstruction pipeline that integrates visual-language models (VLMs) as an intelligent guidance module for 3D Gaussian-based reconstruction. The key insight is that VLMs can provide high-level semantic and photometric understanding across multiple views, enabling informed decisions on where and how Gaussian ellipsoids should be densified. Our method leverages VLMs to guide reconstruction through three core tasks.

First, the VLM evaluates the lighting quality of multi-view images using a dedicated prompt, identifying views prone to geometric degradation due to unfavorable illumination. This enables targeted densification only in necessary regions, improving geometric completeness while avoiding redundant computation. Second, for views affected by underexposure or backlighting, we trigger a three-stage adaptive density control strategy that generates denser Gaussian ellipsoids, leading to more reliable 2D orientation maps and more accurate hair direction supervision.

Third, the VLM estimates hairstyle complexity from multi-view inputs and dynamically determines the maximum Gaussian density required. Complex hairstyles such as tight curls or afro patterns are assigned higher density to capture fine geometric details, while simpler hairstyles are reconstructed with fewer Gaussians. In addition, we introduce a decoupled geometry–texture optimization strategy to mitigate color biases introduced by lighting enhancement.

Geometry and color are jointly optimized using enhanced images to ensure stable geometric reconstruction. Geometry is fixed while color is refined using original images, allowing the recovery of natural hair color and illumination effects. This strategy ensures both geometric precision and visual realism.

Our pipeline further incorporates refinement inspired by prior Gaussian-based hair reconstruction methods. Adaptive Gaussian replication and splitting are applied selectively to balance hole filling and over-densification. Training is supervised using a combination of L1, SSIM, mask, orientation, perceptual, and edge losses, enabling accurate modeling of geometry, texture, and fine structural details while maintaining training efficiency.

We evaluate our method on both self-built and public datasets, comparing against state-of-the-art approaches such as Gaussian Haircut, Im2Haircut, and DiffLocks. Experimental results demonstrate that our method produces more detailed, coherent, and realistic hair reconstructions, particularly under challenging lighting conditions and for complex hairstyles. Quantitative evaluations show consistent improvements over Gaussian Haircut, with PSNR increased by 0.54dB, SSIM improved by 0.004, and LPIPS reduced by 0.01.

Ablation studies further validate the effectiveness of VLM-guided densification, lighting-aware adaptation, and decoupled optimization. In summary, we present a VLM-guided 3D Gaussian hair reconstruction framework that robustly handles non-ideal lighting and complex hairstyles. By integrating semantic analysis, adaptive densification, and decoupled optimization, our method advances the state of the art in photorealistic 3D hair reconstruction and provides a promising direction for future research.