Artificial Intelligence Generated Content(AIGC), known for its superior
visual results, represents a promising mitigation method for high-cost
advertising applications. Numerous approaches have been developed to manipulate
generated content under different conditions. However, a crucial limitation
lies in the accurate description of products in advertising applications.
Applying previous methods directly may lead to considerable distortion and
deformation of advertised products, primarily due to oversimplified content
control conditions. Hence, in this work, we propose a patch-enhanced mask
encoder approach to ensure accurate product descriptions while preserving
diverse backgrounds. Our approach consists of three components Patch Flexible
Visibility, Mask Encoder Prompt Adapter and an image Foundation Model. Patch
Flexible Visibility is used for generating a more reasonable background image.
Mask Encoder Prompt Adapter enables region-controlled fusion. We also conduct
an analysis of the structure and operational mechanisms of the Generation
Module. Experimental results show our method can achieve the highest visual
results and FID scores compared with other methods.

我们提出了一种基于修补增强遮罩编码器的方法，以确保准确的产品描述，并同时保留多样的背景图像。我们的方法包括三个组成部分：补丁灵活可见性、遮罩编码器提示适配器和图像基础模型。实验结果显示，与其他方法相比，我们的方法在视觉结果和 FID 评分方面都能达到最高水平。

增强式补丁编码提示图像生成

Patch-enhanced Mask Encoder Prompt Image Generation

We introduce WonderJourney, a modularized framework for perpetual 3D scene
generation. Unlike prior work on view generation that focuses on a single type
of scenes, we start at any user-provided location (by a text description or an
image) and generate a journey through a long sequence of diverse yet coherently
connected 3D scenes. We leverage an LLM to generate textual descriptions of the
scenes in this journey, a text-driven point cloud generation pipeline to make a
compelling and coherent sequence of 3D scenes, and a large VLM to verify the
generated scenes. We show compelling, diverse visual results across various
scene types and styles, forming imaginary "wonderjourneys". Project website:
this https URL

模块化的 WonderJourney 框架用于生成连续的 3D 场景，利用 LLM 生成场景的文本描述，使用文本驱动的点云生成流程生成连贯的 3D 场景序列，通过大型 VLM 验证生成的场景，展示了多样化的视觉效果。

奇妙之旅：从任何地方到任何地方

WonderJourney: Going from Anywhere to Everywhere

Previous head avatar methods have mostly relied on fixed explicit primitives
(mesh, point) or implicit surfaces (Sign Distance Function) and volumetric
neural radiance field, it challenging to strike a balance among high fidelity,
training speed, and resource consumption. The recent popularity of hybrid field
has brought novel representation, but is limited by relying on parameterization
factors obtained through fixed mappings. We propose GaussianHead: an head
avatar algorithm based on anisotropic 3D gaussian primitives. We leverage
canonical gaussians to represent dynamic scenes. Using explicit "dynamic"
tri-plane as an efficient container for parameterized head geometry, aligned
well with factors in the underlying geometry and tri-plane, we obtain aligned
canonical factors for the canonical gaussians. With a tiny MLP, factors are
decoded into opacity and spherical harmonic coefficients of 3D gaussian
primitives. Finally, we use efficient differentiable gaussian rasterizer for
rendering. Our approach benefits significantly from our novel representation
based on 3D gaussians, and the proper alignment transformation of underlying
geometry structures and factors in tri-plane eliminates biases introduced by
fixed mappings. Compared to state-of-the-art techniques, we achieve optimal
visual results in tasks such as self-reconstruction, novel view synthesis, and
cross-identity reenactment while maintaining high rendering efficiency (0.12s
per frame). Even the pores around the nose are clearly visible in some cases.
Code and additional video can be found on the project homepage.

基于 3D 高斯原理，我们提出了一个名为 GaussianHead 的头像算法，它在头部几何和三面平面的因素对齐变换有效消除了固定映射引入的偏见，使得在自重建、新视图合成和跨身份再现等任务中获得了最佳的可视效果，并保持高渲染效率（每帧 0.12 秒）。有关代码和其他视频可以在项目主页上找到。