ControlNeXt: Powerful and Efficient Control for Image and Video Generation

cs.CV 2025-01 conditional novelty 7.0 of 10

Interactive image editing can be cast as image-to-video generation: initializing from Stable Video Diffusion plus a new matching attention mechanism yields high-quality sketch, drag, and coarse-edit results with far l...

cs.CV 2026-07 conditional novelty 6.5 of 10

A training-free closed-loop PID controller iteratively corrects latent control signals so diffusion models stay consistent with ID, pose, or depth references better than matched open-loop sampling.

cs.CV 2026-07 conditional novelty 6.0 of 10

Appearance pointers are compact tokens that let a diffusion transformer apply text, image, or combined prompts to specific image regions in a single pass.

cs.CV 2026-07 conditional novelty 6.0 of 10

CamoDreamer generates camouflage images by decoupling foreground and background control in a diffusion model, reporting a 15.5-point FID gain over prior state of the art on LAKE-RED.

cs.CV 2026-06 unverdicted novelty 6.0 of 10

Presents a scene-adaptive 3D human image animation framework using ground-adaptive motion retargeting and viewpoint-adaptive latent fusion to control human and camera trajectories, claiming improvements on two benchmarks.

cs.CV 2026-03 conditional novelty 6.0 of 10

GeoDiT is a point-conditioned diffusion transformer that generates satellite imagery from sparse labeled points and claims to beat existing remote sensing generators on FID and SSIM.

cs.CV 2025-09 conditional novelty 6.0 of 10

A training-free video background replacement pipeline that keeps the foreground pixel-consistent by projecting refined latents through a deterministic reparameterization.

math.DS 2025-08 unverdicted novelty 6.0 of 10

The abstract claims a gKYP lemma extension for LPV systems via frequency-range enlargement, but the submitted full text is an unrelated video generation paper.

cs.CV 2025-08 conditional novelty 6.0 of 10

The paper contributes a 12-hour multi-person conversational video dataset with pose and speaking annotations, plus a baseline model for generating full-body talking videos of two to four people.

cs.CV 2025-08 conditional novelty 6.0 of 10

DreamPainter introduces a two-stage diffusion framework trained on a new synthetic e-commerce dataset, DreamEcom-400K, that outperforms open-source inpainting baselines on background generation with text and reference...

cs.CV 2025-06 conditional novelty 6.0 of 10

TurboVSR uses a high-compression video autoencoder with factorized conditioning and non-uniform shortcut sampling to achieve near-state-of-the-art perceptual video super-resolution at roughly 100x lower compute cost.

cs.CV 2025-06 conditional novelty 6.0 of 10

OutDreamer couples a diffusion transformer with mask-driven self-attention and a latent alignment loss to outpaint videos in a zero-shot manner, exceeding prior zero-shot baselines on standard benchmarks.

cs.CV 2025-06 conditional novelty 6.0 of 10

PoseMaster produces a 3D character mesh from one image and a target 3D skeleton, preserving identity and pose in a single unified model, and it outperforms two-stage 2D-to-3D baselines on the VRoid pose canonicalizati...

cs.CV 2025-06 conditional novelty 6.0 of 10

SP-Ctrl improves pose-guided text-to-image generation with sparse poses by learning keypoint embeddings and supervising keypoint attention maps, nearly matching dense depth-based control.

cs.CV 2025-06 conditional novelty 6.0 of 10

A diffusion transformer model generates human-product demonstration videos from paired human and product images while preserving both identities through masked cross-attention and motion template guidance.

cs.CV 2025-05 conditional novelty 6.0 of 10

EF-VI injects temporally expanded end-frame features into a transformer-based image-to-video diffusion model, improving video inbetweening quality over direct fine-tuning and bidirectional sampling baselines.

cs.CV 2025-05 conditional novelty 6.0 of 10

A diffusion model fuses per-person masks with pose keypoints to generate identity-preserving, two-person interactive videos from a single reference image, outperforming prior single-person-animation pipelines.

cs.CV 2025-05 conditional novelty 6.0 of 10

FlexiAct transfers actions from a reference video to an arbitrary target image, allowing changes in layout, skeleton, and viewpoint while keeping the target subject's appearance.

cs.CV 2025-05 conditional novelty 6.0 of 10

PAHA improves audio-driven avatar video generation by re-weighting training loss toward hands and face and by adding audio-video consistency classifiers during inference.

cs.CV 2025-04 conditional novelty 6.0 of 10

Satellite-to-ground generation is extended from single images to consistent multi-view sequences by adding satellite-guided and satellite-temporal conditioning to a frozen latent diffusion model, with a new 100k-pair dataset.

cs.CV 2025-04 conditional novelty 6.0 of 10

Simple conditioning patches and training tricks on the Wan-2.1 model outperform specialized reference-network methods for controllable character animation, according to the paper's benchmarks.

cs.CV 2025-02 conditional novelty 6.0 of 10

AnyCharV is a two-stage diffusion method that places a reference character into a target video scene using pose and mask guidance, with a self-boosting stage that improves identity preservation.

cs.LG 2025-02 conditional novelty 6.0 of 10

FlexControl learns per-timestep, per-block gating for ControlNet control branches, using a FLOPs budget loss to cut compute while preserving or improving image fidelity.

cs.CV 2025-02 conditional novelty 6.0 of 10

A diffusion model that decouples structural features from blur patterns using unpaired target-domain images can deblur photos in unseen domains without paired training data.

cs.CV 2025-01 conditional novelty 6.0 of 10

EchoVideo preserves identity in generated human videos by pre-fusing face, image, and text features, then training with stochastic shallow-feature dropout to reduce copy-paste artifacts.

cs.CV 2025-01 conditional novelty 6.0 of 10

Video Depth Anything adapts Depth Anything V2 to produce temporally consistent depth for arbitrarily long videos using a temporal attention head, an optical-flow-free gradient loss, and key-frame-based stitching.

cs.CV 2025-01 conditional novelty 6.0 of 10

Ingredients adds a mask-supervised identity router to a video diffusion transformer, enabling multi-person videos from a few reference photos without per-identity fine-tuning.

cs.CV 2024-12 conditional novelty 6.0 of 10

FCVG generates stable inbetween frames by injecting linearly interpolated line-match and pose conditions into every denoising step of a pretrained video diffusion model.

cs.CV 2024-12 conditional novelty 6.0 of 10

A drag-style motion control method for 360 degree image-to-video generation, built on spherical trajectory estimation and joint fine-tuning of a pretrained video diffusion model.

cs.CV 2024-12 conditional novelty 6.0 of 10

AM-Adapter injects segmentation-derived matching costs into diffusion self-attention to transfer local object appearances from an exemplar to a target segmentation-driven scene.

cs.CV 2024-12 conditional novelty 6.0 of 10

A 2D-keypoint-conditioned diffusion model trained on a new 10M-image hand dataset enables controllable hand reposing, appearance transfer, novel view synthesis, and zero-shot hand video generation.

cs.CV 2024-12 conditional novelty 6.0 of 10

ControlFace performs zero-shot face rigging from 3DMM renderings using a dual-branch U-Net, a control mixer module, and reference control guidance, and reports the best average DECA re-inference error on FFHQ baselines.

cs.CV 2024-11 conditional novelty 6.0 of 10

A two-stage diffusion pipeline that enriches 2D pose guidance with generated depth and normal maps to achieve state-of-the-art human image animation.

cs.CV 2024-11 conditional novelty 6.0 of 10

StableAnimator uses a video diffusion model with face-embedding adapters and per-step latent optimization to generate pose-driven videos that preserve the reference person's identity end-to-end.

cs.CV 2024-11 conditional novelty 6.0 of 10

TSG classifies real versus synthetic images by feeding the image at a fixed noise timestep through a frozen diffusion U-Net and classifying its predicted noise map.

cs.CV 2024-11 conditional novelty 6.0 of 10

EgoVid-5M is a 5M-clip curated egocentric video dataset with action and kinematic annotations, and EgoDreamer generates egocentric videos from text and camera control signals.

cs.CV 2025-08 conditional novelty 5.0 of 10

NanoControl injects condition-specific key-value pairs into every attention block of Flux via a LoRA-style branch, claiming state-of-the-art controllability at 0.024% extra parameters and 0.029% extra FLOPs.

cs.CV 2025-08 conditional novelty 5.0 of 10

Animate-X++ turns cartoon images into pose-driven animations with text-controlled moving backgrounds, claiming state-of-the-art results on a new synthetic anthropomorphic benchmark.

cs.CV 2025-07 conditional novelty 5.0 of 10

StableAnimator++ combines learnable SVD-guided pose alignment, a distribution-aware ID Adapter, and an HJB-based inference-time face optimizer to preserve identity in human image animation under severe pose misalignment.

cs.CV 2025-07 conditional novelty 5.0 of 10

LiON-LoRA adds a learned scaling token to video-diffusion LoRA adapters, enabling linear and independent control of camera trajectory and object motion strength.

cs.CV 2025-07 conditional novelty 5.0 of 10

FixTalk adds two modules to a real-time GAN talking-head model, decoupling identity from motion to stop identity leakage while using a memory to recover details and reduce artifacts.

cs.GR 2025-06 conditional novelty 5.0 of 10

FramePrompt turns character animation into a video-continuation task by concatenating reference image, skeleton frames, and target frames into one sequence, then training the pretrained Wan-I2V model to generate only ...

cs.CV 2025-06 conditional novelty 5.0 of 10

FullDiT2 accelerates FullDiT-style in-context conditioning for video by dynamic token selection and selective context caching, cutting per-step time by 2-3x with minimal quality loss.

cs.CV 2025-04 conditional novelty 5.0 of 10

DPIR combines lightweight conditioning with global-local CLIP visual prompts and T5 text in an SD3 diffusion transformer to improve image restoration quality.

cs.CV 2025-02 conditional novelty 5.0 of 10

Metric-scale depth alignment plus scene-constrained noise shaping improves camera controllability and video quality for image-to-video generation on RealEstate10K.

eess.IV 2025-02 conditional novelty 5.0 of 10

A temporal diffusion pretraining stage aligned with frame latents improves self-supervised monocular depth estimation in endoscopic video.

cs.CV 2024-12 conditional novelty 5.0 of 10

A single-stage ControlNet on the Lumiere video model, conditioned only on dense point tracks, generalizes to sparse and dense trajectory control for object, camera, and transferred motions.

cs.CV 2024-12 conditional novelty 5.0 of 10

The paper reports a training-free extrapolation method that combines per-patch prompt selection from cross-attention masks, ControlNet edge guidance, and permuted dilated sampling to reduce repetition and distortion a...

cs.CV 2024-12 conditional novelty 5.0 of 10

CPA adds camera pose control to OpenSora-style diffusion video models by encoding a sparse Plücker motion field into a latent and injecting it into temporal attention layers.

cs.CV 2024-12 conditional novelty 4.0 of 10

Concatenating multiple video clips into one input and fine-tuning a LoRA adapter lets a pretrained video diffusion transformer produce consistent multi-scene videos from a single prompt.

cs.CV 2024-12 conditional novelty 4.0 of 10

FLT improves identity preservation in landmark-conditioned character animation by combining reference face shape with driving expressions in 3D and re-rendering landmarks.

cs.CV 2024-12 conditional novelty 4.0 of 10

A diffusion transformer with joint attention over all camera views and frames, a lightweight BEV controller, and a re-weighted object loss reports state-of-the-art multi-view driving video generation (FVD 37.8 on nuScenes).

cs.LG 2025-02 conditional novelty 2.0 of 10

The paper organizes research on efficient diffusion models into a taxonomy spanning algorithms, systems, and frameworks, and provides a curated reference list.

cs.CL 2025-01 conditional novelty 2.0 of 10

A survey that categorizes and summarizes parameter-efficient fine-tuning methods across large language, vision, and multimodal models.

cs.CV 2024-11 conditional novelty 1.0 of 10

A decade-spanning survey categorizes over 440 text-to-image papers by architecture, research problem, dataset, and evaluation metric.