2307.04684 FreeDrag Point Tracking is Not You Need for Interactive Point-based Image Editing

FreeDrag: Point Tracking is Not You Need for Interactive Point-based Image Editing §

Authors: Pengyang Ling, Lin Chen, Pan Zhang, Huaian Chen, Yi Jin

Abstract: To serve the intricate and varied demands of image editing, precise and flexible manipulation of image content is indispensable. Recently, DragGAN has achieved impressive editing results through point-based manipulation. However, we have observed that DragGAN struggles with miss tracking, where DragGAN encounters difficulty in effectively tracking the desired handle points, and ambiguous tracking, where the tracked points are situated within other regions that bear resemblance to the handle points. To deal with the above issues, we propose FreeDrag, which adopts a feature-oriented approach to free the burden on point tracking within the point-oriented methodology of DragGAN. The FreeDrag incorporates adaptive template features, line search, and fuzzy localization techniques to perform stable and efficient point-based image editing. Extensive experiments demonstrate that our method is superior to the DragGAN and enables stable point-based editing in challenging scenarios with similar structures, fine details, or under multi-point targets.

What, Why and How §

Here is a summary of the key points in this paper:

What:

• The paper proposes a new method called FreeDrag for interactive point-based image editing using generative adversarial networks (GANs).

Why:

• Existing methods like DragGAN have issues with missed tracking and ambiguous tracking of handle points during iterative editing.

• Missed tracking happens when handle points disappear or move to incorrect locations between iterations due to drastic image changes.

• Ambiguous tracking occurs when multiple similar points exist, causing confusion in tracking the correct handle point.

• These issues of unstable point tracking lead to inaccurate editing results.

How:

• FreeDrag introduces adaptive template features to record handle point features, eliminating precise point tracking.

• It uses fuzzy localization with line search to restrict handle point movements, preventing ambiguity.

• Handle point motion is broken into smaller sub-motions based on distance and feature variation for stability.

• Experiments show FreeDrag avoids missed/ambiguous tracking issues, enabling more robust point-based editing, especially for images with similar structures or drastic edits.

In summary, FreeDrag proposes adaptive template features and fuzzy localization to address the limitations of previous point-based GAN image editing methods. It demonstrates superior stability and precision for interactive editing of images.

Main Contributions §

Here are the main contributions of this paper:

• Identified limitations of previous point-based GAN image editing method DragGAN, including missed tracking and ambiguous tracking issues.

• Proposed the FreeDrag framework to address these limitations through new techniques:

• Adaptive template features - Record handle point features without precise tracking. Features are updated selectively based on motion quality.

• Fuzzy localization with line search - Customize handle point motion into sub-motions based on distance and features to provide stability. Restrict search to line between points to avoid ambiguity.

• Controlled optimization - Set maximum steps and early stopping based on feature differences to improve efficiency.

• Demonstrated the effectiveness of FreeDrag through experiments on StyleGAN-generated images showing improved stability and precision compared to DragGAN.

• Showed robust performance on challenging cases like images with similar structures or requiring drastic edits that cause issues for previous methods.

• Proposed techniques are generalizable beyond GANs to other generative models like diffusion models.

In summary, the key contributions are identifying limitations of prior arts, proposing the FreeDrag framework to address those limitations, and experimentally demonstrating its improvements for precise, stable point-based image editing.

Method Section §

Here is a summary of the method section of the paper:

The paper proposes the FreeDrag framework which has three main components:

Adaptive Template Features:

• Maintain a feature template F_ema for each handle point to track features during iterative editing.

• F_ema is updated as an exponential moving average based on motion quality each iteration.

• This records handle point features without precise tracking, providing stability.

Fuzzy Localization via Line Search:

• Break overall handle point motion into smaller sub-motions towards customized locations.

• Locations are chosen based on distance and feature difference from target.

• Search is constrained to a line between current and target locations.

• Provides suitable optimization gradient and reduces ambiguity.

Controlled Optimization:

• Limit number of steps per sub-motion.

• Pause early if feature differences are small.

• Assign stationary features when motion terminates.

• Improves efficiency.

Additionally:

• Directional editing is achieved using user-provided masks.

• Termination signal based on remaining distance to target handles early stopping.

In summary, the key aspects of the FreeDrag method are the adaptive template features for stability, fuzzy localization to customize sub-motions, and controlled optimization for efficiency. Together these address limitations of prior point-based image editing techniques.

High-Level Pseudo Code §

Here is the high-level pseudo code for the FreeDrag method:

# Input: image I, handle points pi, target points ti
 
# Initialization: 
F_ema0 = features of pi
t_i0 = pi
 
# Iterative editing:
for k in range(max_iterations):
 
  # Adaptive template features:
  F_rk = features of t_ik
  F_emak = update_ema(F_emak-1, F_rk) 
  
  # Fuzzy localization:
  t_ik+1 = localize(t_ik, ti, F_emak, d, l)  
 
  # Controlled optimization:
  optimize(I, t_ik+1, F_emak) 
  if early_stop_condition:
    break
 
  # Update:
  t_ik = t_ik+1
 
# Termination:
if remaining_distance(t_ik, ti) < threshold:
  stop = True
 
# Functions:
  
def update_ema(F_emak-1, F_rk):
  # Update EMA features based on Eq. 1
  
def localize(t_ik, ti, F_emak, d, l):
  # Search next location based on Eq. 6
  
def optimize(I, t_ik+1, F_emak):
  # Optimize image I based on Eq. 5
  # For max 5 steps or early stopping
  
def remaining_distance(t_ik, ti):
  # Compute distance between current 
  # and target locations

In summary, the key steps are iterative editing with adaptive template features, fuzzy localization of points, controlled optimization, and termination based on distance.

Detailed Pseudo Code §

Here is a more detailed pseudo code to implement the FreeDrag method:

# Inputs
I = image 
P = {p1, p2,...pn} # Handle points
T = {t1, t2,...tn} # Target points
M = mask # Editable region mask
 
# Hyperparameters
r = radius # For extracting local features
d = max_distance # For localization
l = expected_feature_diff # For localization
 
# Initialization
F0 = extract_features(I) 
 
for i in range(n):
  F_emai0 = F0[pi] # Features of handle point
  t_i0 = pi
  
# Iterative Editing Loop
for k in range(max_iterations):
 
  for i in range(n):
  
    # Adaptive Template Features
    F_rik = extract_features(I, t_ik, r)
    λ = get_lambda(F_emai-1, F_rik) 
    F_emai = λ*F_rik + (1-λ)*F_emai-1
    
    # Fuzzy Localization
    if distance(t_ik, ti) < d:
      t_ik+1 = t_i 
    else:
      t_ik+1 = localize(t_ik, ti, F_emai, d, l)
    
    # Controlled Optimization 
    loss = ||F_emai - F_rik|| + mask_loss
    optimize(I, loss) 
    if early_stop(F_emai, F_rik):
      break
      
  # Termination Check
  if all_close(T, t_ik):
    break
  
# Functions  
 
def get_lambda(F_emai-1, F_rik):
  # Compute λ based on Eq. 4
 
def localize(t_ik, ti, F_emai, d, l):
  # Return next location based on Eq. 6
  
def optimize(I, loss):
  # Optimize I for max 5 steps
  # Use Adam optimizer
  
def early_stop(F_emai, F_rik):
  # Return True if features close
  
def all_close(T, t_ik):
  # Check if all handle points are close to targets

This shows the key steps like updating adaptive features, fuzzy localization, controlled optimization, and termination in more detail. The overall iterative editing process with stability and efficiency improvements is visible.