Boosting Camera Motion Control for Video Diffusion Transformers

Recent advancements in diffusion models have significantly enhanced the quality of video generation. However, fine-grained control over camera pose remains a challenge. While U-Net-based models have shown promising results for camera control, transformer-based diffusion models (DiT)-the preferred architecture for large-scale video generation - suffer from severe degradation in camera motion accuracy. In this paper, we investigate the underlying causes of this issue and propose solutions tailored to DiT architectures. Our study reveals that camera control performance depends heavily on the choice of conditioning methods rather than camera pose representations that is commonly believed. To address the persistent motion degradation in DiT, we introduce Camera Motion Guidance (CMG), based on classifier-free guidance, which boosts camera control by over 400%. Additionally, we present a sparse camera control pipeline, significantly simplifying the process of specifying camera poses for long videos. Our method universally applies to both U-Net and DiT models, offering improved camera control for video generation tasks.

ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNet

This paper introduces ViscoNet, a novel method that enhances text-to-image human generation models with visual prompting. Unlike existing methods that rely on lengthy text descriptions to control the image structure, ViscoNet allows users to specify the visual appearance of the target object with a reference image. ViscoNet disentangles the object's appearance from the image background and injects it into a pre-trained latent diffusion model (LDM) model via a ControlNet branch. This way, ViscoNet mitigates the style mode collapse problem and enables precise and flexible visual control. We demonstrate the effectiveness of ViscoNet on human image generation, where it can manipulate visual attributes and artistic styles with text and image prompts. We also show that ViscoNet can learn visual conditioning from small and specific object domains while preserving the generative power of the LDM backbone.

UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer

Existing person image generative models can do either image generation or pose transfer but not both. We propose a unified diffusion model, UPGPT to provide a universal solution to perform all the person image tasks - generative, pose transfer, and editing. With fine-grained multimodality and disentanglement capabilities, our approach offers fine-grained control over the generation and the editing process of images using a combination of pose, text, and image, all without needing a semantic segmentation mask which can be challenging to obtain or edit. We also pioneer the parameterized body SMPL model in pose-guided person image generation to demonstrate new capability - simultaneous pose and camera view interpolation while maintaining a person's appearance. Results on the benchmark DeepFashion dataset show that UPGPT is the new state-of-the-art while simultaneously pioneering new capabilities of edit and pose transfer in human image generation.

Pose Guided Multi-person Image Generation From Text

Transformers have recently been shown to generate high quality images from texts. However, existing methods struggle to create high fidelity full-body images, especially multiple people. A person's pose has a high degree of freedom that is difficult to describe using words only; this creates errors in the generated image, such as incorrect body proportions and pose. We propose a pose-guided text-to-image model, using pose as an additional input constraint. Using the proposed Keypoint Pose Encoding (KPE) to encode human pose into low dimensional representation, our model can generate novel multi-person images accurately representing the pose and text descriptions provided, with minimal errors. We demonstrate that KPE is invariant to changes in the target image domain and image resolution; we show results on the Deepfashion dataset and create a new multi-person Deepfashion dataset to demonstrate the multi-capabilities of our approach.