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Yifei Xia

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4 papers

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NeurIPS Conference 2025 Conference Paper

Dense Metric Depth Estimation via Event-based Differential Focus Volume Prompting

Boyu Li
Peiqi Duan
Zhaojun Huang
Xinyu Zhou
Yifei Xia
Boxin Shi

Dense metric depth estimation has witnessed great developments in recent years. While single-image-based methods have demonstrated commendable performance in certain circumstances, they may encounter challenges regarding scale ambiguities and visual illusions in real world. Traditional depth-from-focus methods are constrained by low sampling rates during data acquisition. In this paper, we introduce a novel approach to enhance dense metric depth estimation by fusing events with image foundation models via a prompting approach. Specifically, we build Event-based Differential Focus Volumes (EDFV) using events triggered through focus sweeping, which are subsequently transformed into sparse metric depth maps. These maps are then utilized for prompting dense depth estimation via our proposed Event-based Depth Prompting Network. We further construct synthetic and real-captured datasets to facilitate the training and evaluation of both frame-based and event-based methods. Quantitative and qualitative results, including both in-domain and zero-shot experiments, demonstrate the superior performance of our method compared to existing approaches. Code and data will be available at https: //github. com/liboyu02/EDFV/.

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NeurIPS Conference 2025 Conference Paper

PanoWan: Lifting Diffusion Video Generation Models to 360$^\circ$ with Latitude/Longitude-aware Mechanisms

Yifei Xia
Shuchen Weng
Siqi Yang
Jingqi Liu
Chengxuan Zhu
Minggui Teng
Zijian Jia
Han Jiang

Panoramic video generation enables immersive 360$^\circ$ content creation, valuable in applications that demand scene-consistent world exploration. However, existing panoramic video generation models struggle to leverage pre-trained generative priors from conventional text-to-video models for high-quality and diverse panoramic videos generation, due to limited dataset scale and the gap in spatial feature representations. In this paper, we introduce PanoWan to effectively lift pre-trained text-to-video models to the panoramic domain, equipped with minimal modules. PanoWan employs latitude-aware sampling to avoid latitudinal distortion, while its rotated semantic denoising and padded pixel-wise decoding ensure seamless transitions at longitude boundaries. To provide sufficient panoramic videos for learning these lifted representations, we contribute PanoVid, a high-quality panoramic video dataset with captions and diverse scenarios. Consequently, PanoWan achieves state-of-the-art performance in panoramic video generation and demonstrates robustness for zero-shot downstream tasks.

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AAAI Conference 2025 Conference Paper

PlaNet: Learning to Mitigate Atmospheric Turbulence in Planetary Images

Yifei Xia
Chu Zhou
Chengxuan Zhu
Chao Xu
Boxin Shi

Obtaining planetary images with good visual quality is not an easy task since they are usually degenerated by atmospheric turbulence during the imaging procedure. Existing atmospheric turbulence mitigation methods designed for conventional images cannot be applied to planetary images, since the objects on the Earth have totally different degeneration patterns to planets. Besides, in planetary imaging, photographers often capture as many frames as possible to reduce the noise level of planetary images, which requires the method designed for planetary images to support an arbitrary number of input frames. In this paper, we propose a vertical distance-aware turbulence simulation pipeline to synthesize realistic planetary images in accordance with their unique degeneration patterns at a large scale with affordable computational cost, and design a neural network to mitigate the turbulence with flexible input frames by adopting an edge-based supervision strategy to handle the background scarcity issue. Experimental results show that our method achieves state-of-the-art performance on both synthetic and real-world images.

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NeurIPS Conference 2024 Conference Paper

Efficient Multi-task LLM Quantization and Serving for Multiple LoRA Adapters

Yifei Xia
Fangcheng Fu
Wentao Zhang
Jiawei Jiang
Bin Cui

With the remarkable achievements of large language models (LLMs), the demand for fine-tuning and deploying LLMs in various downstream tasks has garnered widespread interest. Parameter-efficient fine-tuning techniques represented by LoRA and model quantization techniques represented by GPTQ and AWQ are of paramount significance. However, although these techniques have been widely adopted in single-task scenarios, research is scarce in multi-task scenarios. To be specific, we find that mainstream quantization methods would prevent the base LLM from being shared among tasks, so current LLM serving systems are infeasible to integrate LLM quantization with multiple LoRA adapters to achieve memory-efficient multi-task serving. Moreover, existing LLM serving systems lack support for dynamic task addition and overlook the workload differences among tasks, leading to inefficiencies in multi-task scenarios. This work proposes LoRA-Inlaid, an efficient multi-task LLM serving system. On the one hand, LoRA-Inlaid designs a flexible and efficient multi-task quantization algorithm (MLGPTQ) that facilitates the sharing of a single quantized model for multiple LoRA adapters, which significantly reduces the memory consumption for model deployment. Meanwhile, it supports adding LoRA adapters for new tasks on the fly, without sacrificing the stability of online services. On the other hand, LoRA-Inlaid develops a novel multi-task scheduling algorithm guided by output length prediction and grouping among different tasks, which effectively shrinks the memory consumption and avoids frequent switching of LoRA adapters. Empirical results verify that LoRA-Inlaid outperforms existing state-of-the-art LLM serving systems by up to 1. 58 times in terms of throughput, 1. 76 times in terms of average latency, 2 times in terms of job completion time, and 10 times in terms of SLO Attainment, while maintaining the same level of model quality.

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