美图Computer Vision Algorithm Intern (Image / Video Human Body Aesthetic Enhancement)
任职要求
Experience · PhD candidate in Computer Science, Artificial Intelligence, Intelligent Arts, or related fields · Proficient in Python, with experience using common libraries (e.g., NumPy, Transformers, Diffusers, Open3D), and strong hands-on experience with deep learning frameworks such as PyTorch · Strong, sustained interest in image/video aesthetics or 3D art; resilient mindset and openness to change Skills & Abilities · Solid engineering implementation skills and rigorous experimental practices; able to independently complete module-level tasks · Proficient with AI-assisted programming tools; maintains clean, well-structured code · Strong ability to read, analyse, summarize, and reproduce academic papers · Clear logical thinking, strong problem-decomposition skills, and curiosity about underlying principles Motivation & Values · Long-term interest in AI + imaging / 3D / aesthetics · Proactive, self-driven, and willing to explore new approaches and ch…
工作职责
About the Team We are a visual R&D team focused on human aesthetic modelling and advanced 3D vision research. Our work spans human image understanding, 3D reconstruction, and intelligent aesthetic enhancement. By combining academic research methodologies with real-world product deployment, we continuously explore new frontiers in AI-driven image/video generation, editing, spatiotemporal consistency, and 3D structural understanding. Location & Duration Sydney Central; 6-12 months Role Overview You will participate in the research and development of human aesthetic enhancement and spatiotemporally consistent editing technologies at Meitu. You will work directly with real, product-scale datasets and state-of-the-art algorithms. Depending on the internship track, your work may include (but is not limited to): · Fine-grained and controllable image / video aesthetic enhancement · 2D / 3D human tracking and 3D reconstruction · Regression, reconstruction, and structural constraints of digital human models (e.g., SMPL) This role offers the opportunity to produce both production-ready technical outcomes and high-quality academic research results. It is a research-and-engineering-oriented internship, ideal for candidates with strong interest and capability in 3D vision fundamentals, human visual quality enhancement, video generation models, and 3D human modelling. Key Responsibilities · Research and implement algorithms related to depth estimation, multi-view generation, and 2D / 3D tracking with spatiotemporal reconstruction · Follow state-of-the-art 3D vision papers and open-source projects; reproduce experiments and adapt methods to practical applications · Collaborate with data teams to refine the 3D aesthetic development pipeline, improve data collection and quality evaluation, and establish foundations for high-quality scaling · Explore the integration of human structure priors (Skeleton / SMPL / Mesh) with multi-modal cues such as depth, normals, and optical flow in reconstruction and generative models · Assist in building data processing, evaluation, and visualization tools (e.g., immersive video aesthetic editing) to support rapid iteration · Enable high-quality projection of 3D features into 2D visual outputs, with the goal of producing A-level or above academic publications
岗位概述 负责深度学习、计算机视觉及 AIGC 方向的算法研究与工程落地，涵盖图像/视频的生成、编辑、增强、分割、重建等核心视觉技术。你将跟踪前沿学术进展，将研究成果转化为真实可用的产品能力。 本岗位为 2026 年暑期实习，全职实习时长 5 个月以上。实习期间表现优秀者，将有机会获得留用。 职责描述 - 跟踪并探索 CV/AIGC 前沿技术，参与将相关技术应用到图像/视频生成、编辑、增强或视觉内容理解任务，持续提升模型效果 - 参与视觉模型（如 Diffusion Models、GAN、ViT 等）的算法设计、模型训练、微调与性能优化，推动下游影像功能落地 - 针对实际应用场景，设计并优化算法效果、效率与鲁棒性 - 核心代码实现，输出 Demo 或 SDK，根据产品反馈迭代优化 - 研究领域最新技术，撰写技术专利或论文（如有机会） 任职资格 - 计算机科学、人工智能、电子信息、数学等相关专业，硕士及以上学历（特别优秀的本科生亦可） - 具备 CV 相关研究或项目经验，熟悉图像生成、分割、目标检测、人脸/人体技术或视频处理等至少一个子领域 - 熟练掌握 Python，熟悉 PyTorch 等深度学习框架，良好的数学与编程基础 - 对 AI 技术充满热情，熟练使用各种 AI 提效工具，持续关注 AIGC 前沿研究 - 具备良好的沟通能力和团队协作精神 加分项 - 在 CVPR、ICCV、ECCV、NeurIPS 等 CV/ML 相关顶级会议或期刊发表论文 - 了解 C++ 并有相关工程实践经验 ▸ Overview You'll work at the intersection of deep learning, computer vision, and AIGC research — bringing cutting-edge techniques, from image generation and video editing to segmentation and reconstruction, into real products. This is a full-time summer 2026 internship (5+ months). Strong performers will be considered for a return offer. ▸ Responsibilities - Track and explore the latest CV/AIGC research; apply breakthroughs to image/video generation, editing, enhancement, and visual understanding tasks - Contribute to the design, training, fine-tuning, and optimization of visual models (Diffusion Models, GANs, ViT, etc.); drive downstream feature deployment - Design and optimize algorithms for real-world scenarios — focusing on effectiveness, efficiency, and robustness - Implement core algorithms; deliver demos or SDKs; iterate based on product feedback - Stay current with research trends; contribute to patents or publications when opportunities arise ▸
岗位概述 负责深度学习、计算机视觉及 AIGC 方向的算法研究与工程落地，涵盖图像/视频的生成、编辑、增强、分割、重建等核心视觉技术。你将跟踪前沿学术进展，将研究成果转化为真实可用的产品能力。 本岗位为 2026 年暑期实习，全职实习时长 5 个月以上。实习期间表现优秀者，将有机会获得留用。 职责描述 - 跟踪并探索 CV/AIGC 前沿技术，参与将相关技术应用到图像/视频生成、编辑、增强或视觉内容理解任务，持续提升模型效果 - 参与视觉模型（如 Diffusion Models、GAN、ViT 等）的算法设计、模型训练、微调与性能优化，推动下游影像功能落地 - 针对实际应用场景，设计并优化算法效果、效率与鲁棒性 - 核心代码实现，输出 Demo 或 SDK，根据产品反馈迭代优化 - 研究领域最新技术，撰写技术专利或论文（如有机会） 任职资格 - 计算机科学、人工智能、电子信息、数学等相关专业，硕士及以上学历（特别优秀的本科生亦可） - 具备 CV 相关研究或项目经验，熟悉图像生成、分割、目标检测、人脸/人体技术或视频处理等至少一个子领域 - 熟练掌握 Python，熟悉 PyTorch 等深度学习框架，良好的数学与编程基础 - 对 AI 技术充满热情，熟练使用各种 AI 提效工具，持续关注 AIGC 前沿研究 - 具备良好的沟通能力和团队协作精神 加分项 - 在 CVPR、ICCV、ECCV、NeurIPS 等 CV/ML 相关顶级会议或期刊发表论文 - 了解 C++ 并有相关工程实践经验 ▸ Overview You'll work at the intersection of deep learning, computer vision, and AIGC research — bringing cutting-edge techniques, from image generation and video editing to segmentation and reconstruction, into real products. This is a full-time summer 2026 internship (5+ months). Strong performers will be considered for a return offer. ▸ Responsibilities - Track and explore the latest CV/AIGC research; apply breakthroughs to image/video generation, editing, enhancement, and visual understanding tasks - Contribute to the design, training, fine-tuning, and optimization of visual models (Diffusion Models, GANs, ViT, etc.); drive downstream feature deployment - Design and optimize algorithms for real-world scenarios — focusing on effectiveness, efficiency, and robustness - Implement core algorithms; deliver demos or SDKs; iterate based on product feedback - Stay current with research trends; contribute to patents or publications when opportunities arise ▸
岗位概述 负责深度学习、计算机视觉及 AIGC 方向的算法研究与工程落地，涵盖图像/视频的生成、编辑、增强、分割、重建等核心视觉技术。你将跟踪前沿学术进展，将研究成果转化为真实可用的产品能力。 本岗位为 2026 年暑期实习，全职实习时长 5 个月以上。实习期间表现优秀者，将有机会获得留用。 职责描述 - 跟踪并探索 CV/AIGC 前沿技术，参与将相关技术应用到图像/视频生成、编辑、增强或视觉内容理解任务，持续提升模型效果 - 参与视觉模型（如 Diffusion Models、GAN、ViT 等）的算法设计、模型训练、微调与性能优化，推动下游影像功能落地 - 针对实际应用场景，设计并优化算法效果、效率与鲁棒性 - 核心代码实现，输出 Demo 或 SDK，根据产品反馈迭代优化 - 研究领域最新技术，撰写技术专利或论文（如有机会） 任职资格 - 计算机科学、人工智能、电子信息、数学等相关专业，硕士及以上学历（特别优秀的本科生亦可） - 具备 CV 相关研究或项目经验，熟悉图像生成、分割、目标检测、人脸/人体技术或视频处理等至少一个子领域 - 熟练掌握 Python，熟悉 PyTorch 等深度学习框架，良好的数学与编程基础 - 对 AI 技术充满热情，熟练使用各种 AI 提效工具，持续关注 AIGC 前沿研究 - 具备良好的沟通能力和团队协作精神 加分项 - 在 CVPR、ICCV、ECCV、NeurIPS 等 CV/ML 相关顶级会议或期刊发表论文 - 了解 C++ 并有相关工程实践经验 ▸ Overview You'll work at the intersection of deep learning, computer vision, and AIGC research — bringing cutting-edge techniques, from image generation and video editing to segmentation and reconstruction, into real products. This is a full-time summer 2026 internship (5+ months). Strong performers will be considered for a return offer. ▸ Responsibilities - Track and explore the latest CV/AIGC research; apply breakthroughs to image/video generation, editing, enhancement, and visual understanding tasks - Contribute to the design, training, fine-tuning, and optimization of visual models (Diffusion Models, GANs, ViT, etc.); drive downstream feature deployment - Design and optimize algorithms for real-world scenarios — focusing on effectiveness, efficiency, and robustness - Implement core algorithms; deliver demos or SDKs; iterate based on product feedback - Stay current with research trends; contribute to patents or publications when opportunities arise ▸
Design and develop generative video models for high-fidelity, controllable synthesis. Build infrastructure for large-scale training, evaluation, and benchmarking of video models. Investigate model consolidation and shared representation learning across video understanding and generation tasks. Optimize algorithms for runtime, power, memory, and temporal quality on-device. Collaborate with product and research teams to integrate video generation technologies into Apple’s camera and video pipelines.