versatile-controls-video-diffusion

name	versatile-controls-video-diffusion
title	Enabling Versatile Controls for Video Diffusion Models
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2503.16983
keywords	["Video Generation","Diffusion Models","Conditional Generation","Control Signals","Edge Detection"]
description	Enable flexible control over video diffusion models through multi-modal control signals (edges, masks, poses) without retraining. Apply lightweight Transformer-based auxiliary modules to add Canny edge, segmentation, and pose constraints to frozen pre-trained generators.

Core Concept

VCtrl extends video diffusion models with versatile control capabilities by injecting control signal information through lightweight auxiliary modules while keeping the base generator frozen. This training-efficient approach enables precise control over spatial and temporal aspects of generated videos—whether from edge maps, semantic masks, or human poses—without expensive model retraining.

Architecture Overview

The VCtrl framework consists of three main components working together:

• Control Encoding Pipeline: Accepts video-based control signals (Canny edges, segmentation masks, keypoints) and transforms them into latent representations with task-aware mask sequences that enhance adaptability
• VCtrl Auxiliary Module: A lightweight Transformer Encoder (approximately one-fifth the size of the base network) processes control information and integrates it with base network features through DistAlign layers
• Sparse Residual Injection: Control signals inject at fixed intervals via trainable parallel branches, using adaptive average pooling to align spatial/temporal dimensions before merging through residual fusion

Implementation

Control Signal Preprocessing

Before feeding control signals into the VCtrl module, apply hierarchical filtering to ensure data quality. This includes visual quality assessment using image metrics, CLIP score validation for semantic alignment, and task-specific preprocessing (Canny edge detection with hysteresis thresholds and Gaussian smoothing for edges, semantic segmentation for masks, and 133-keypoint pose estimation for human motion).

import cv2
import numpy as np
from PIL import Image

def preprocess_canny_edges(video_frames, threshold1=50, threshold2=150):
    """Convert RGB video frames to Canny edge maps for control."""
    edge_maps = []
    for frame in video_frames:
        gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
        edges = cv2.Canny(gray, threshold1, threshold2)
        # Apply Gaussian smoothing to refine edges
        smoothed = cv2.GaussianBlur(edges, (5, 5), 1.0)
        edge_maps.append(smoothed)
     np.stack(edge_maps)

 ():
    
    masks = []
     frame  video_frames:
         use_semantic:
            
            mask = np.zeros_like(frame[:,:,])
        masks.append(mask)
     np.stack(masks)