Recent advances in text-to-3D generation have been remarkable, with methods
such as DreamFusion leveraging large-scale text-to-image diffusion-based models
to supervise 3D generation. These methods, including the variational score
distillation proposed by ProlificDreamer, enable the synthesis of detailed and
photorealistic textured meshes. However, the appearance of 3D objects generated
by these methods is often random and uncontrollable, posing a challenge in
achieving appearance-controllable 3D objects. To address this challenge, we
introduce IPDreamer, a novel approach that incorporates image prompts to
provide specific and comprehensive appearance information for 3D object
generation. Our results demonstrate that IPDreamer effectively generates
high-quality 3D objects that are consistent with both the provided text and
image prompts, demonstrating its promising capability in
appearance-controllable 3D object generation.

最新的文字到 3D 生成技术取得了显著进展，现有的方法利用大规模的文字到图像扩散模型，如 DreamFusion，来监督 3D 生成。然而，这些方法生成的 3D 对象的外观通常是随机和不可控的，这在实现可控外观的 3D 对象方面是一个挑战。为了解决这个问题，我们引入了 IPDreamer，一种新颖的方法，它结合了图像提示，为 3D 对象生成提供特定和全面的外观信息。我们的结果表明，IPDreamer 有效地生成了与所提供的文本和图像提示一致的高质量 3D 对象，展示了它在可控外观的 3D 对象生成方面的潜能。